Enumerating permutation polynomials over finite fields by degree II
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名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱(共包含二级节点15 个,三级节点93 个)间序列分析)监督学习)领域 二级分类 三级分类。
2021年4月第28卷第4期控制工程Control Engineering of ChinaApr.2021Vol.28,No.4文章编号:1671-7848(2021)04-0665-07DOI: 10.14107/ki.kzgc.20190644基于R PM D E-M K S V M的锂离子电池剩余使用寿命预测简献忠l a,韦进l b,王如志2(1.上海理工大学a.光电信息与计算机工程学院;b.机械工程学院,上海200090;2.北京工业大学材料科学与工程学院,北京100020)摘要:为了提高锂离子电池剩余使用寿命预测的精度,提出了一种基于随机参数机制差分进化(random parameter machine differential evolution,R P M D E)算法与多核支持向量机(multi-kernel support vector machine,M K S V M)的锂离子电池剩余使用寿命预测模型。
首先,将差分变异策略和随机搜索算子引入差分进化算法中来增强算法种群多样性,提高全局搜索能力。
然后,通过R P M D E算法优化M K S V M的惩罚因子和核参数来提高预測模型的精度。
最后,利用美国国家航空航天局的锂离子电池測试数据验证R P M D E-M K S V M模型的准确性。
实验结果表明,相比于差分进化算法模型和粒子群优化算法模型,R P M D E-M K S V M模型不仅具有更快的收敛速度,而且具有更优的预測精度。
关键词:锂离子电池;剩余使用寿命预测;R P M D E算法;多核支持向量机中图分类号:T M912 文献标示码:ARemaining Useful Life Prediction of Lithium-ion Battery Based onRPMDE-MKSVMJ I A N X i a n-z h o n g x\ W E I J i n l b,W A N G R u-zhi2(1.a.School of Optical-electrical and Computer Engineering;b.School of Mechanical Engineering,University of Shanghai forScience and Technology,Shanghai200090, China;2.School of Materials Science and Engineering,Beijing University ofTechnology,Beijing 100020, China)Abstract:In order to improve the prediction accuracy of remaining useful life(R U L)of lithium-ion battery,a R U L prediction model of lithium-ion battery based on ran d o m parameter machine differential evolution (R P M D E)algorithm and multi-kernel support vector machine(M K S V M)is proposed in this paper.Firstly,the differential mutation strategy and rand o m search operator are introduced into the differential evolution (D E) algorithm to enhance the diversity of the algorithm population and improve the global search ability.T h e n, R P M D E algorithm is used to optimize the penalty factors and kernel parameters of M K S V M to improve the accuracy of the prediction m o d e l.Finally,the battery test data of National Aeronautics and Space Administration(N A S A)are used to verify the accuracy of R P M D E-M K S V M m o d e l.T h e experimental results s h o w that R P M D E-M K S V M model not only has faster convergence speed,but also has better prediction accuracy compared with D E algorithm model and particle s w a r m optimization algorithm m o d e l.K e y w o r d s:Lithium-ion battery;remaining useful life prediction;R P M D E algorithm;multi-kernel support vector machineOi引言随着新能源发电技术的发展,国内外已有很多 学者和工程技术人员开展了新能源发电管理系统方 面的研究。
第32卷第2期206年6月金陵科技学院学报J O U R N A L O F J IN U N G IN S T IT U T E O F T E C H N O L O G YV o l.32,N o.2J unc,2016图像奇异性表征机理分析比较郑玮(金陵科技学院计算机工程学院,江苏南京211169)摘要:图像奇异性包含的许多重要信息对于图像的进一步分析具有重要作用。
对于图像中的不同奇异性通常需要采用不同的方法表示。
小波变换和曲波变换作为稀疏表示中的重要方法,具有广泛的应用。
分析了小波变换和曲波变换对于图像奇异性表证的不同效果。
实验结果和理论分析均表明小波变换对于图像中的点奇异性具有很好效果,但对于线奇异性表示则不够稀疏,曲波则可以高效地表示图像边缘的曲线奇异性。
关键词:稀疏表示;图像分类;稀疏编码;寺征编码;小波;曲波中图分类号:T P391 文献标志码:A文章编号:1672 755X(2016)02 0034 05Image Singularity Analysis and ComparisonZH EN G Wei(Jinling Institute of Technology,Nanjing 211169, China)Abstra c t: Singularity in images carry massive essential inform ation,which is crucial to furtheranalysis. Wc need to adopt different methods according to the elements in image singularity.Wavelet transform and curvelet transform are two important methods for sparse representationand are widely used in numerous fields. In this paper,wavelet transform and curvelettransform are used to analyze the effects of different singularity in images. The experimentalresults and theoretical analysis both dem onstrate that wavelet is good at noises in images butcannot represent the edges effectively. On the contrary,curvelet is very suitable for the edge.Key word s :sparse representation ;image classification ;sparse coding ;feature coding ;w avelet; curvelet图像奇异性表征机理分析主要是指对图像噪声点、图像边缘信息的分析。
(0,2) 插值||(0,2) interpolation0#||zero-sharp; 读作零井或零开。
0+||zero-dagger; 读作零正。
1-因子||1-factor3-流形||3-manifold; 又称“三维流形”。
AIC准则||AIC criterion, Akaike information criterionAp 权||Ap-weightA稳定性||A-stability, absolute stabilityA最优设计||A-optimal designBCH 码||BCH code, Bose-Chaudhuri-Hocquenghem codeBIC准则||BIC criterion, Bayesian modification of the AICBMOA函数||analytic function of bounded mean oscillation; 全称“有界平均振动解析函数”。
BMO鞅||BMO martingaleBSD猜想||Birch and Swinnerton-Dyer conjecture; 全称“伯奇与斯温纳顿-戴尔猜想”。
B样条||B-splineC*代数||C*-algebra; 读作“C星代数”。
C0 类函数||function of class C0; 又称“连续函数类”。
CA T准则||CAT criterion, criterion for autoregressiveCM域||CM fieldCN 群||CN-groupCW 复形的同调||homology of CW complexCW复形||CW complexCW复形的同伦群||homotopy group of CW complexesCW剖分||CW decompositionCn 类函数||function of class Cn; 又称“n次连续可微函数类”。
Cp统计量||Cp-statisticC。
密码学报ISSN2095-7025CN10-1195/TN Journal of Cryptologic Research,2019,6(5):639–642©《密码学报》编辑部版权所有.E-mail:jcr@ Tel/Fax:+86-10-82789618完全置换多项式专栏完全置换多项式专栏序言(中英文)曾祥勇湖北大学数学与统计学学院应用数学湖北省重点实验室,武汉430062通信作者:曾祥勇,E-mail:xzeng@中图分类号:TP309.7文献标识码:A DOI:10.13868/ki.jcr.000329中文引用格式:曾祥勇.完全置换多项式专栏序言(中英文)[J].密码学报,2019,6(5):639–642.英文引用格式:ZENG X Y.Preface of complete permutation polynomials column[J].Journal of Cryptologic Research,2019,6(5):639–642.Preface of Complete Permutation Polynomials ColumnZENG Xiang-YongHubei Key Laboratory of Applied Mathematics,Faculty of Mathematics and Statistics,Hubei University, Wuhan430062,ChinaCorresponding author:ZENG Xiang-Yong,E-mail:xzeng@置换多项式在分组密码算法设计中具有广泛的应用.一般情况下,分组密码算法中明密文之间的关系就是密钥控制下的置换.另外,密码算法的许多重要组成部分也是置换.例如,具有良好密码学性质的置换常被用来设计对称密码算法中唯一的非线性部件S盒.完全置换多项式是一类特殊的置换,其概念是Mann在上个世纪四十年代提出的,早期的研究结果与正交拉丁方联系紧密.完全置换多项式具有优良的密码学性质.例如,偶特征有限域上的完全置换多项式只有一个不动点.此外,非线性完全置换具有良好的位独立性和雪崩特性,使得基于完全置换的密码算法具备良好的扩散和混淆作用.因此,完全置换多项式的研究具有重要的理论和实际意义.完全置换多项式在密码学中的较早应用是由美国Teledyne电子技术公司的Mittenthal提出的.他分别在1995年和1997年发表的论文《Block substitutions using orthomorphic mapping》和专利《Nonlinear dynamic substitution devices and methods for block substitutions employing coset decompositions and direct geometric generation》中讨论了完全置换的构造和基本性质,首次公开了如何使用完全置换多项式来设计密码算法以及非线性动力系统装置.这些成果为完全置换在密码学中的应用奠定了基础,并使人们对完全置换多项式这一数学对象产生浓厚的兴趣.在随后的十年里,国内外学者在这一研究方向上取得了一系列进展.在密码应用方面比较有代表性成果的是Vaudenay在1999年证明了添加完全置换或几乎完全置换的Lai-Massey结构具有更好的伪随机性.另外,国内的标志性成果是2006年公布的基于完全置换的分组密码算法SMS4,该算法被指定用于无线局域网WAPI且被我国商用密码管理局确定为国家密码行业标准,在密码行业中有着极为重要的作用.收稿日期:2019-09-16定稿日期:2019-10-11640Journal of Cryptologic Research密码学报Vol.6,No.5,Oct.20192007年之后的几年里,完全置换多项式的研究进展非常缓慢,主要原因是判断多项式的完全置换性质是一个十分困难的问题,即使最简单的单项式的完全置换性质也不容易被刻画.直到2014年,Tu,Zeng 和Hu提出了用加法特征和极坐标表示的方法来将完全置换单项式的问题转化成有限域上特殊方程解的问题.受到其启发,从2015年开始国际学术界重新兴起了研究有限域上完全置换多项式的热潮.在最近五年,涌现了一大批有代表性的成果,例如基于例外多项式、AGW准则或密码结构的完全置换.完全置换多项式在流密码、Hash函数、编码设计、校验位系统设计等领域也有一定的应用.虽然最近几年完全置换多项式的研究已经取得了一系列进展,但是已有的完全置换多项式类依然十分稀少,完全置换多项式的研究尚处于初级阶段,还存在许多有待进一步研究的重要问题,其中完全置换多项式的构造以及相关密码学性质分析是该方向的重点研究内容.在本期“完全置换多项式”专栏中共收录三篇文章,其中包含一篇综述论文和两篇研究论文,希望对完全置换多项式的理论和应用研究起到促进作用.第一篇是综述论文《完全置换多项式的研究进展》.该论文较全面地总结分析了近二十多年来有限域上完全置换多项式的相关理论研究成果,从完全置换多项式的构造方法和多项式的形式出发给出了已有完全置换的分类,阐述了完全置换多项式的存在性、代数次数、圈结构以及广义完全置换多项式的相关研究进展.此外,还指出了一些值得进一步研究的问题.该论文是一篇较好的介绍完全置换多项式的综述,对想了解完全置换多项式的研究者有很高的参考价值和很好的指导意义.第二篇论文题目是《有限域上几类置换和完全置换》.因为判定一个多项式构成完全置换的条件是相当复杂的,所以研究特殊类型的完全置换多项式具有重大的意义.该论文运用迹函数、线性置换和Dickson置换构造了有限域F q n上六类形如γx+Tr q n q(h(x))的置换多项式,证明了其中三类为完全置换并分析了其余三类不构成完全置换的原因.另外,在已知的置换判定法则基础上他们还研究了形如xh(x s)的二项式的完全置换性质,得到了有限域上几类新的完全置换.第三篇论文题目是《有限域上完全置换多项式的构造》.稀疏型完全置换多项式因其具有简洁的代数表达式以及便于硬件实现的特点而备受关注.该论文构造了特征2有限域F q2上形如xh(x q−1)q+1的两类完全置换多项式,给出了这些多项式是完全置换多项式的充要条件或者充分条件.通过选取适当的函数,得到了几类完全置换三项式和完全置换七项式.两篇研究论文均考虑完全置换多项式的构造问题,研究成果丰富了已有完全置换多项式的构造,具有重要的理论意义.Permutation polynomials are widely used in the design of block cipher algorithms.In general,the relationship between plaintext and ciphertext in block cipher algorithms is a permutation under the control of keys.In addition,many important components of cryptographic algorithms are permuta-tions.For example,permutations with good cryptographic properties are often used to design S-box which is the unique nonlinear component of symmetric cryptographic algorithms.Complete permutation polynomials are a special class of permutation polynomials.The concept of complete permutation polynomials was proposed by Mann in the1940s.Earlier research results are closely related to orthogonal Latin plete permutation polynomials have good crypto-graphic properties.For example,complete permutation polynomials over finite fields of even charac-teristic have a single fixed point.Moreover,nonlinear complete permutations have bit independence and avalanche characteristics,so the cryptographic algorithms based on complete permutations have good diffusion and confusion effects.Therefore,the research of complete permutation polynomials has important theoretical and practical significance.The early application of complete permutation polynomials in cryptography was proposed by Mit-tenthal who comes from Teledyne Electronics Technology Company of the United States.He published the paper“Block substitutions using orthomorphic mapping”in1995and the patent“Nonlinear dy-namic substitution devices and methods for block substitutions employing coset decompositions and direct geometric generation”in1997,respectively.In this paper and this patent,the constructions and曾祥勇:完全置换多项式专栏序言(中英文)641properties of complete permutations were discussed.In addition,he firstly presented how to use com-plete permutation polynomials to design cryptographic algorithms and nonlinear dynamic substitution devices.These achievements laid the foundation for the application of complete permutations in cryp-tography and aroused great interest in the mathematical object complete permutation polynomials. In the following ten years,scholars worldwide have made a series of achievements in this field.The representative achievement in cryptographic application is that Vaudenay proved that the Lai-Massey scheme with complete permutations or almost complete permutations has better pseudo-randomness in1999.In addition,the landmark achievement in China is that the block cipher algorithm SMS4 was designed by use of complete permutations and published in2006.This algorithm has been desig-nated for WAPI in WLAN and has been designated as the national cryptographic industry standard by China’s Commercial Cryptographic Administration.It plays an extremely important role in the cryptographic industry.In the years after2007,the research on complete permutation polynomials has developed very slowly,since the problem of judging a polynomial to be a complete permutation is very difficult, even for the simplest monomials.In2014,Tu,Zeng,and Hu proposed the method of using the additive characters of the underlying finite fields and the technique of polar coordinate representation to transform the problem of complete permutation monomials over finite fields into that of determining the number of the solutions to certain equations over finite fields.Inspired by their works,a new upsurge of studying complete permutation polynomials over finite fields has arisen in the international academic circles in2015.In the past five years,a large number of representative achievements have emerged, such as complete permutations based on exceptional polynomials,the AGW criteria or cryptographic structures.Complete permutation polynomials also have some applications in stream ciphers,Hash functions, coding design,check digit systems,and other fields.In recent years,a series of achievements have been obtained in the study of complete permutation polynomials,but the known classes of complete permutation polynomials are very rare.The research of complete permutation polynomials is still in primary stage,and there exist many important problems which need to be studied in the future.The construction of complete permutation polynomials and the analysis of cryptographic properties for these polynomials are the key research problems in this field.The special column“Complete permutation polynomials”has collected three papers involving one review article and two research articles,hoping to promote the development of this field.The first paper is review article“Overview on complete permutation polynomials”.This paper comprehensively summarizes and analyzes the related theoretical research results of complete permu-tation polynomials over finite fields in the past twenty years,and gives the classification of known complete permutation polynomials from the construction methods and the form of polynomials.The existence,algebraic degree,cycle structure of complete permutation polynomials and generalized com-plete permutation polynomials are also discussed.In addition,some problems worthy to study in the future are pointed out.This paper is a good summary of complete permutation polynomials.It is of a high reference value and a good guiding significance for researchers who intend to learn about complete permutation polynomials.The second paper is“A few classes of permutations and complete permutations over finite fields”. To characterize the conditions of a polynomial to be a complete permutation is very difficult,so it is of great significance to study complete permutation polynomials with special forms.This paper constructs six classes of permutations with the formγx+Tr q n q(h(x))by using some trace functions, linear permutations,and Dickson permutations.Three of them are proved to be complete computations and they give the reasons why the other three types are not complete permutation polynomials.In642Journal of Cryptologic Research 密码学报Vol.6,No.5,Oct.2019addition,based on the known criteria of permutations,they study the permutation properties of binomials with the form xh (x s )and obtain a few new classes of complete permutation binomials over finite fields.The third paper is “Construction of complete permutation polynomials over finite fields”.Sparse complete permutation polynomials have attracted much attention due to their concise algebraic expres-sions and features for easy implementation on hardware.This paper constructs two classes of complete permutation polynomials of the form xh (x q −1)q +1over F q 2with characteristic 2and characterizes the necessary and sufficient conditions or sufficient conditions for these polynomials to be complete per-mutation polynomials.By choosing appropriate functions,several types of complete permutation trinomials and complete permutation with seven terms are obtained.The construction of complete permutation polynomials is considered in both research articles.The research results enrich the existing constructions of complete permutation polynomials and have important theoretical significance.作者信息曾祥勇(1973–),博士,教授,博士生导师.主要研究领域为密码与编码.xzeng@ ZENG Xiang-Yong (1973–),Ph.D.,Pro-fessor,Doctoral Tutor.The main research covers cryptology and coding.xzeng@。
origin计算赝电容赝电容是指在某些材料中由于界面效应或电荷分布不均匀等原因而产生的等效电容。
在计算赝电容时,可以采用原子尺度的第一性原理计算方法,如密度泛函理论(DFT)等。
首先,进行赝电容的计算需要确定材料的晶体结构。
可以通过实验技术如X射线衍射或电子显微镜等手段得到晶体结构参数,或者使用计算方法如晶体结构预测等来获取晶体结构信息。
接下来,需要进行电子结构计算。
可以使用密度泛函理论(DFT)等第一性原理方法来计算材料的电子结构。
DFT方法可以通过求解Schrödinger方程来得到材料中电子的能级分布和电荷密度分布等信息。
在电子结构计算中,需要选择合适的赝势(pseudopotential)来描述电子与离子核的相互作用。
赝势是一种有效的近似方法,可以用较少的自由度来描述离子核的运动,从而减小计算量。
选择合适的赝势可以保证计算结果的准确性。
计算得到材料的电子结构后,可以通过计算电荷密度分布来获得材料内部的电荷分布情况。
赝电容的计算通常涉及到界面效应,因此需要考虑材料的界面结构和界面电荷分布情况。
赝电容的计算还需要考虑材料的几何结构和电场分布。
可以通过构建模型或使用实验测量得到的几何结构来进行计算。
同时,可以通过引入外界电场来模拟实际应用中的电场分布情况。
最后,可以使用数值方法如有限元法或有限差分法等来计算赝电容。
这些方法可以将材料的几何结构、电子结构和电场分布等信息输入计算模型,并求解相应的方程,从而得到赝电容的数值结果。
需要注意的是,赝电容的计算是一个复杂的过程,结果的准确性受到多个因素的影响,如材料的晶体结构、电子结构计算的方法和参数选择、赝势的选择以及模型的建立等。
因此,在进行赝电容计算时,需要仔细选择计算方法和参数,并进行合理的验证和分析。
一、PolynomialFeatures简介PolynomialFeatures是scikit-learn中的一个函数,用于生成一个新的特征矩阵,其中包含所有的组合特征,用于多项式回归或其他高阶模型。
在机器学习中,有时候通过添加高次项自变量的幂函数来拟合一些非线性模型。
这时候可以使用PolynomialFeatures函数来自动生成这些高次项的特征变量。
二、PolynomialFeatures的基本用法在scikit-learn中使用PolynomialFeatures函数非常简单,只需按照以下步骤即可:1.导入PolynomialFeatures函数在代码中首先需要导入PolynomialFeatures函数,一般位于`sklearn.preprocessing`模块中。
2.创建PolynomialFeatures对象接下来需要创建一个PolynomialFeatures对象,可以通过指定`degree`参数来设置生成的多项式阶数,还可以设置`include_bias`参数来决定是否包含截距项。
3.拟合与转换将原始的特征矩阵传入PolynomialFeatures对象的`fit_transform`方法,即可得到包含多项式特征的新特征矩阵。
4.应用到模型中可以将生成的多项式特征矩阵应用到机器学习模型中进行训练和预测。
三、PolynomialFeatures的参数说明PolynomialFeatures函数主要包含以下几个常用参数:1. degree:整数型参数,表示多项式的阶数。
2. interaction_only:布尔型参数,默认为False,表示是否只生成特征的交互项,若为True则只生成特征之间的乘积项。
3. include_bias:布尔型参数,默认为True,表示是否在生成的多项式特征中包含截距项。
四、示例代码以下是一个简单的示例代码,演示了如何使用PolynomialFeatures生成多项式特征。
收稿日期:2020-11-25基金项目:福建省自然科学基金(2016J01032)作者简介:程金发(1966-),男,江西省乐平市人,博士,教授,博士生导师.*通信作者.E-mail :***************.cn非一致格子上离散分数阶差分与分数阶和分程金发*(厦门大学数学科学学院福建厦门,361005)摘要:众所周知,一致格子上分数阶和分与分数阶差分的思想概念也是最近几年才兴起的,并且在该邻域得到了很大的发展.但是在非一致格子x ()z =c 1z 2+c 2z +c 3或者x ()z =c 1q z +c 2q -z +c 3上,分数阶和分与分数阶差分的定义是什么,这是一个十分复杂和有趣的问题.本文首次提出非一致格子上分数阶和分与Riemann-Liouville 分数阶差分、Caputo 分数阶差分的定义以及非一致格子上广义Abel 积分方程的求解等基础性结果.关键词:超几何差分方程;非一致格子;分数阶和分;分数阶差分;特殊函数中图分类号:33C45;33D45;26A33;34K37文献标志码:A文章编号:2095-7122(2021)01-0001-013On the fractional sum and fractional difference on nonuniform latticesCHENG Jinfa *(School of Mathematical Sciences,Xiamen University,Xiamen,Fujian 361005,China )Abstract:As is well known,the idea of a fractional sum and difference on uniform lattice is more current,and gets a lot of development in this field.But the definitions of fractional sum and fractional difference of f ()z on nonuniform lattices x ()z =c 1z 2+c 2z +c 3or x ()z =c 1q z +c 2q -z +c 3seem much more complicated andinteresting.In this article,for the first time we propose the definitions of the fractional sum and fractional difference on nonuniform lattices.The solution of the generalized Abel equation is obtained etc.Key words:special function;orthogonal polynomials;adjoint difference equation;difference equation of hy-pergeometric type;nonuniform lattice第34卷第1期2021年3月闽南师范大学学报(自然科学版)Journal of Minnan Normal University (Natural Science )Vol.34No.1Mar.20211背景回顾及问题提出正如我们在本文序言指出的,分数阶微积分的概念几乎与经典微积分同时起步,可以回溯到Euler 和Leibniz 时期.经过几代数学家的努力,特别是近几十年来,分数阶微积分已经取得了惊人的发展和广阔的应用,有关分数阶微积分的著作层出不穷,例如文献[1-4],但是在一致格子x ()z =z 和x ()z =q z 或者q -z ,z ∈C 上关于离散分数阶微积分的思想,仍然是最近才兴起的.虽然关于一致格子x ()z =z 和x ()z =q z 的离散分数微积分出现和建立相对较晚,但是该领域目前已经做出了大量的工作,且取得了很大的发展[5-8].在最近十年的学术著作中,程金发[9],Goodrich 和Peterson [10]相继出版了两本有关离散分数阶方程理论、离散分数微积分的著作,其中全面系统地介绍了离散分数微积分的基本定义和基本定理,以及最新的参考资料.有关q -分数阶微积分方面的著作可参见Annaby 和Mansour [11].非一致格子的定义回溯到超几何型微分方程[12-13]:σ()z y ′′()z +τ()z y ′()z +λy ()z =0,(1)的逼近,这里σ()z 和τ()z 分别是至多二阶和一阶多项式,λ是常数.Nikiforov 等[14-15]将式(1)推广到如下最一般的复超几何差分方程σˉ[]x ()s ΔΔx ()s -12éëêùûú∇y ()s ∇x ()s +12τˉ[]x ()s éëêùûúΔy ()s Δx ()s +∇y ()s ∇x ()s +λy ()s =0,(2)这里σˉ()x 和τˉ()x 分别是关于x ()s 的至多二阶和一阶多项式,λ是常数,Δy ()s =y ()s +1-y ()s ,∇y ()s =y ()s -y ()s -1,并且x ()s 必须是以下非一致格子.定义1[16-17]两类格子函数x ()s 称之为非一致格子,如果它们满足x ()s =-c 1s 2+-c 2s +-c 3,(3)x ()s =c 1q s +c 2q -s +c 3,(4)这里c i ,-c i 是任意常数,且c 1c 2≠0,-c 1-c 2≠0.当c 1=1,c 2=c 3=0,或c 2=1,c 1=c 3=0或者-c 2=1,-c 1=-c 3=0时,这两种格子函数x ()s :x ()s =s ,(5)x ()s =q s 或x ()s =q -s(6)称之为一致格子.给定函数F ()s ,定义关于x γ()s 的差分或差商算子为∇γF ()s =∇F ()s ∇x γ()s ,且∇k γF ()z =∇∇x γ()z ()∇∇x γ+1()z ⋯()∇()F ()z ∇x γ+k -1()z .()k =1,2,⋯关于差商算子,命题1是常用的.命题1给定两个复函数f ()s ,g ()s ,成立恒等式Δυ()f ()s g ()s =f ()s +1Δυg ()s +g ()s Δυf ()s =g ()s +1Δυf ()s +f ()s Δυg ()s ,Δυ()f ()s g ()s =g ()s +1Δυf ()s -f ()s +1Δυg ()s g ()s g ()s +1=g ()s Δυf ()s -f ()s Δυg ()s g ()s g ()s +1,Δυ()f ()s g ()s =f ()s -1Δυg ()s +g ()s Δυf ()s =g ()s -1Δυf ()s +f ()s Δυg ()s ,(7)Δυ()f ()s g ()s =g ()s -1Δυf ()s -f ()s -1Δυg ()s g ()s g ()s -1=闽南师范大学学报(自然科学版)2021年2g ()s Δυf ()s -f ()s Δυg ()s g ()s g ()s -1.我们必须指出,在非一致格子式(3)或者式(4),即使当n ∈N ,如何建立非一致格子的n -差商公式,也是一件很不平凡的工作,因为它是十分复杂的,也是难度很大的.事实上,在文献[14-15]中,Nikiforov 等利用插值方法得到了如下n -阶差商∇()n 1[]f ()s 公式:定义2[12-13]对于非一致格子式(3)或式(4),让n ∈N +,那么∇()n 1[]f ()s =∑k =0n ()-1n -k[]Γ()n +1q[]Γ()k +1q[]Γ()n -k +1q×∏l =0n∇x []s +k -()n -12∇x []s +()k -l +12f ()s -n +k =∑k =0n()-1n -k[]Γ()n +1q[]Γ()k +1q[]Γ()n -k +1q×∏l =0n ∇x n +1()s -k ∇x []s +()n -k -l +12f ()s -k ,(8)这里[]Γ()s q 是修正的q -Gamma 函数,它的定义是[]Γ()s q=q -()s -1()s -24Γq ()s ,并且函数Γq ()s 被称为q -Gamma 函数;它是经典Euler Gamma 函数Γ()s 的推广.其定义是Γq ()s =ìíîïïïï∏k =0∞(1-q k +1)()1-q s -1∏k =0∞(1-q s +k),当||q <1;q -()s -1()s -22Γ1q ()s ,当||q >1.(9)经过进一步化简后,Nikiforov 等在文献[14]中将n 阶差分∇()n 1[]f ()s 的公式重写成下列形式:定义3[14]对于非一致格子式(3)或式(4),让n ∈N +,那么∇()n 1[]f ()s =∑k =0n ()[]-n qk[]k q ![]Γ()2s -k +c q[]Γ()2s -k +n +1+c qf ()s -k ∇x n +1()s -k ,这里[]μq=γ()μ=ìíîïïïïq u2-q -u 2q 12-q -12如果x ()s =c 1q s +c 2q -s +c 3;μ,如果x ()s =-c 1s 2+-c 2s +c 3,(10)且c =ìíîïïïïïïïïlog c 2c 1log q ,当x ()s =c 1q s +c 2q -s +c 3,-c 2-c 1,当x ()s =-c 1s 2+-c 2s +c 3.程金发:非一致格子上离散分数阶差分与分数阶和分第1期3现在存在两个十分重要且具有挑战性的问题需要进一步深入探讨:1)对于非一致格子上超几何差分方程式(2),在特定条件下存在关于x ()s 多项式形式的解,如果用Rodrigues 公式表示的话,它含有整数阶高阶差商.一个新的问题是:若该特定条件不满足,那么非一致格子上超几何差分方程式(2)的解就不存在关于x ()s 的多项式形式,这样高阶整数阶差商就不再起作用了.此时非一致格子超几何方程的解的表达形式是什么呢?这就需要我们引入一种非一致格子上分数阶差商的新概念和新理论.因此,关于非一致格子上α-阶分数阶差分及α-阶分数阶和分的定义是一个十分有趣和重要的问题.显而易见,它们肯定是比整数高阶差商更为难以处理的困难问题,自专著[14-15]出版以来,Nikiforov 等并没有给出有关α-阶分数阶差分及α-阶分数阶和分的定义,我们能够合理给出非一致格子上分数阶差分与分数阶和分的定义吗?2)另外,我们认为作为非一致格子上最一般性的离散分数微积分,它们也会有独立的意义,并可以导致许多有意义的结果和新理论.本文的目的是探讨非一致格子上离散分数阶和差分.受文章篇幅所限,本文我们仅合理给出非一致格上分数阶和分与分数阶差分的基本定义,其它更多结果例如:非一致格子离散分数阶微积分的一些基本定理,如:Euler Beta 公式,Cauchy Beta 积分公式,Taylor 公式、Leibniz 公式在非一致格子上的模拟形式,非一致格子上广义Abel 方程的解,以及非一致格子上中心分数差分方程的求解,离散分数阶差和分与非一致格子超几何方程之间联系等内容,请参见笔者新专著[16].2非一致格子上的整数和分与整数差分设x ()s 是非一致格子,这里s ∈ℂ.对任意实数γ,x γ()s =x ()s +γ2也是一个非一致格子.让∇γF ()s =f ()s .那么F ()s -F ()s -1=f ()s []x γ()s -x γ()s -1.选取z ,a ∈ℂ,和z -a ∈N .从s =a +1到z ,则有F ()z -F ()a =∑s =a +1zf ()s ∇x r()s .因此,我们定义∫a +1z f ()s d ∇x γ()s =∑s =a +1zf ()s ∇xγ()s .容易直接验证下列式子成立.命题2给定两个复变函数F ()z ,f ()z ,这里复变量z ,a ∈C 以及z -a ∈N ,那么成立1)∇γéëêùûú∫a +1zf ()s d ∇x γ()s =f ()z ;2)∫a +1z∇γF ()s d ∇x γ()s =F ()z -F ()a .现在让我们定义非一致格子上的广义n -阶幂函数[]x ()s -x ()z ()n 为[]x ()s -x ()z ()n =∏k =0n -1[]x ()s -x ()z -k ,()n ∈N +,当n 不是正整数时,需要将广义幂函数加以进一步推广,它的性质和作用是非常重要的,非一致格子上广义幂函数[]x γ()s -x γ()z ()α的定义如下:闽南师范大学学报(自然科学版)2021年4定义4[17-18]设α∈C ,广义幂函数[]x γ()s -x γ()z ()α定义为[]x γ()s -x γ()z ()α=ìíîïïïïïïïïïïïïïïïïïïïïΓ()s -z +a Γ()s -z ,如果x ()s =s ,Γ()s -z +a Γ()s +z +γ+1Γ()s -z Γ()s +z +γ-α+1,如果x ()s =s 2,()q -1αq α()γ-α+12Γq ()s -z +αΓq ()s -z ,如果x ()s =q s ,12α()q -12αq -α()s +γ2Γq ()s -z +αΓq ()s +z +γ+1Γq ()s -z Γq ()s +z +γ-α+1,如果x ()s =q s +q -s 2.(11)对于形如式(4)的二次格子,记c =-c 2-c 1,定义[]x γ()s -x γ()z ()α=-c 1αΓ()s -z +a Γ()s +z +γ+c +1Γ()s -z Γ()s +z +γ-α+c +1;(12)对于形如式(3)的二次格子,记c =logc 2c 1log q,定义[]xγ()s -x γ()z ()α=éëùûc 1()1-q 2αq -α()s +γ2Γq()s -z +a Γq()s +z +γ+c +1Γq()s -z Γq()s +z +γ-α+c +1,(13)这里Γ()s 是Euler Gamma 函数,且Γq ()s 是Euler q -Gamma 函数,其定义如式(9).命题3[17-18]对于x ()s =c 1q s +c 2q -s +c 3或者x ()s =-c 1s 2+-c 2s +-c 3,广义幂[]x γ()s -x γ()z ()α满足下列性质:[]x γ()s -x γ()z []x γ()s -x γ()z -1()μ=[]x γ()s -x γ()z ()μ[]xγ()s -x γ()z -μ=(14)[]xγ()s -x γ()z ()μ+1;(15)[]xγ-1()s +1-x γ-1()z ()μ[]xγ-μ()s -x γ-μ()z =[]x γ-μ()s +μ-x γ-μ()z []x γ-1()s -x γ-1()z ()μ=[]x γ()s -x γ()z ()μ+1;(16)ΔzΔx γ-μ+1()z []xγ()s -x γ()z ()μ=-∇s∇x γ+1()s []x γ+1()s -x γ+1()z ()μ=(17)-[]μq []x γ()s -x γ()z ()μ-1;(18)∇z∇x γ-μ+1()z {}1[]xγ()s -x γ()z ()μ=-ΔsΔx γ-1()s ìíîïïüýþïï1[]x γ-1()s -x γ-1()z ()μ=(19)[]μq[]xγ()s -x γ()z ()μ+1(20)这里[]μq 定义如式(10).程金发:非一致格子上离散分数阶差分与分数阶和分第1期5现在让我们详细给出非一致格子x γ()s 上整数阶和分的定义,这对于我们进一步给出非一致格子x γ()s 上分数阶和分的定义是十分有帮助的.设γ∈R ,对于非一致格子x γ()s ,数集{}a +1,a +2,⋯,z 中f ()z 的1-阶和分定义为y 1()z =∇-1γf ()z =∫a +1z f ()s d ∇x γ()s ,(21)这里y 1()z =∇-1γf ()z 定义在数集{}a +1,mod ()1中.那么由命题2,我们有∇1γ∇-1γf ()z =∇y 1()z ∇x γ()z =f ()z ,(22)并且对于非一致格子x γ()s ,数集{}a +1,a +2,⋯,z 中f ()z 的2-阶和分定义为y 2()z =∇-2γf ()z =∇-1γ+1[]∇-1γf ()z =∫a +1z y 1()s d ∇x γ+1()s =∫a +1z d ∇x γ+1()s ∫a +1s f ()t d ∇x γ()t =∫a +1z f ()t d ∇x γ()t ∫tz d ∇x γ+1()s =∫a +1z []x γ+1()z -x γ+1()t -1f ()s d ∇x γ()s .(23)这里y 2()z =∇-2γf ()z 定义在数集{}a +1,mod ()1中.同时,可得∇1γ+1∇1γ-1y 1()z =∇y 2()z ∇x γ+1()z =y 1()z ,∇2γ∇-2γf ()z =∇∇x γ()z ()∇y 2()z ∇x γ+1()z =∇y 1()z ∇x γ()z =f ()z .(24)更一般地,由数学归纳法,对于非一致格子x γ()s ,数集{}a +1,a +2,⋯,z 中函数f ()z ,我们可以给出函数f ()z 的n -阶和分定义为y k ()z =∇-kγf ()z =∇-1γ+k -1[]∇-()k -1γf ()z =∫a +1z y k -1()s d ∇x γ+k -1()s =1[]Γ()k q∫a +1z []xγ+k -1()z -x γ+k -1()t -1()k -1f ()t d ∇x γ()t ,()k =1,2,⋯(25)这里[]Γ()k q=ìíîïïq -()k -1()k -2Γq ()k ,如果x ()s =c 1q s +c 2q -s +c 3;Γ()α,如果x ()s =-c 1s 2+-c 2s +c 3,这满足下式[]Γ()k +1q=[]k q []Γ()k q ,[]Γ()2q =[]1q []Γ()1q =1.那么成立∇kγ∇-k γf ()z =∇∇x γ()z ()∇∇x γ+1()z ⋯()∇y k ()z ∇x γ+k -1()z =f ()z .()k =1,2,⋯(26)需要指出的是,当k ∈C 时,式(25)右边仍然是有意义的,因此自然地,我们就可以对非一致格子x γ()s 闽南师范大学学报(自然科学版)2021年6给出函数f ()z 的分数阶和分定义如下:定义5(非一致格子分数阶和分)对任意Re α∈R +,对于非一致格子式(3)和式(4),数集{}a +1,a +2,⋯,z 中的函数f ()z ,我们定义它的α-阶分数阶和分为∇-αγf ()z =1[]Γ()αq∫a +1z []xγ+α-1()z -x γ+α-1()t -1()α-1f ()s d ∇x γ()s ,(27)这里[]Γ()αq=ìíîïïq -()s -1()s -2Γq ()α,如果x ()s =c 1q s +c 2q -s +c 3;Γ()α,如果x ()s =-c 1s 2+-c 2s +c 3,这满足下式[]Γ()α+1q=[]αq []Γ()αq .3非一致格子上的Abel 方程及分数阶差分非一致格子x γ()s 上f ()z 的分数阶差分定义相对似乎更困难和复杂一些.我们的思想是起源于非一致格子上广义Abel 方程的求解.具体来说,一个重要的问题是:让m -1<Re α≤m ,定义在数集{}a +1,a +2,⋯,z 的f ()z 是一给定函数,定义在数集{}a +1,a +2,⋯,z 的g ()z 是一未知函数,它们满足以下广义Abel 方程∇-αγg ()z =∫a +1z []x γ+α-1()z -x γ+α-1()t -1()α-1[]Γ()αqg ()t d ∇x γ()t =f ()t ,(28)怎样求解该广义Abel 方程式(28)?为了求解方程式(28),我们需要利用重要的Euler Beta 公式在非一致格子下的基本模拟.定理1[16](非一致格子上Euler Beta 公式)对于任何α,β∈C ,那么对非一致格子x ()s ,我们有∫a +1z []x β()z -x β()t -1()β-1[]Γ()βq[]x ()t -x ()αα[]Γ()α+1qd ∇x 1()t =[]x β()z -x β()α()α+β[]Γ()α+β+1q.(29)定理2(Abel 方程的解)设定义在数集{}a +1,mod ()1中的函数f ()z 和函数g ()z 满足∇-αγg ()z =f ()z ,0<m -1<Re α≤m ,那么g ()z =∇m γ∇-m +αγ+αf ()z (30)成立.证明我们仅需证明∇-m γg ()z =∇-m +αγ+αf ()z ,即∇-()m -αγ+αf ()z =∇-()m -αγ+α∇-αγg ()z =∇-m γg ()z .事实上,由定义5可得程金发:非一致格子上离散分数阶差分与分数阶和分第1期7∇-()m -αγ+af ()z =∫a +1z []xγ+m -1()z -x γ+m -1()t -1()m -α-1[]Γ()m -αqf ()t d ∇x γ+α()t =∫a +1z []x γ+m -1()z -x γ+m -1()t -1()m -α-1[]Γ()m -αqd ∇x γ+α()t ⋅∫a +1z []xγ+α-1()t -x γ+α-1()s -1()α-1[]Γ()αqg ()s d ∇x γ()s =∫a +1zg ()s ∇x γ()s ∫sz []xγ+m -1()z -x γ+m -1()t -1()m -α-1[]Γ()m -αq⋅[]xγ+α-1()t -x γ+α-1()s -1()α-1[]Γ()αqd ∇x γ+α()t .在定理1中,将α+1替换成s ;α替换成α-1;β替换成m -α,且将x ()t 替换成x γ+α-1()t ,那么x β()t 替换成x γ+m -1()t ,则我们能够得出下面的等式∫sz []xγ+m -1()z -x γ+m -1()t -1()m -α-1[]Γ()m -αq[]xγ+α-1()t -x γ+α-1()s -1()α-1[]Γ()αqd ∇x γ+α()t =[]xγ+m -1()z -x γ+m -1()s -1()-m -1[]Γ()m q,因此,我们有∇-()m -αγ+af ()z =∫a +1z []x γ+m -1()z -x γ+m -1()s -1()-m -1[]Γ()m qg ()s d ∇x γ()s =∇-mγg ()z ,这样就有∇m γ∇-()m -αγ+a f ()z =∇m γ∇-m γg ()z =g ()z .由定理2得到启示,很自然地我们给出关于f ()z 的Riemann-Liouville 型α-阶()0<m -1<Re α≤m 分数阶差分的定义如下:定义6(Riemann-Liouville 分数阶差分)让m 是超过Re α的最小正整数,对于非一致格子x γ()s ,数集{}α,mod ()1中f ()z 的Riemann-Liouville 型α-阶分数阶差分定义为∇αγf ()z =∇m γ()∇α-mγ+αf ()z .(31)形式上来说,在定义5中,如果α替换成-α,那么式(27)的右边将变为∫a +1z []xγ-α-1()z -x γ-α-1()t -1()-α-1[]Γ()-αqf ()t d ∇x γ()t =∇∇x γ-α()t ()∇∇x γ-α+1()t ⋯()∇∇x γ-α+n -1()t ⋅∫a +1z[]xγ+n -α-1()z -x γ+n -α-1()t -1()n -α-1[]Γ()n -αqf ()t d ∇x γ()t =∇n γ-α∇-n +αγf ()z =∇αγ-αf ()z .(33)闽南师范大学学报(自然科学版)2021年8从式(33),我们也可以得到f ()z 的Riemann-Liouville 型α-阶分数阶差分如下:定义7(Riemann-Liouville 型分数阶差分2)对任意Re α>0,对于非一致格子x γ()s ,数集{}a +1,a +2,⋯,z 中f ()z 的Riemann-Liouville 型α-阶分数阶差分定义为∇αγ-αf ()z =∫a +1z x γ-α-1()z -x γ-α-1()t -1()-α-1[]Γ()-αqf ()t d ∇x γ()t ,(34)将∇γ-α()t 替换成∇γ()t ,那么∇αγf ()z =∫a +1z []x γ-1()z -x γ-1()t -1()-α-1[]Γ()-αqf ()t d ∇x γ+α()t ,(35)这里假定[]Γ()-αq ≠0.4非一致格子上Caputo 型分数阶差分在本节,我们将给出非一致格子上Caputo 型分数阶差分的合理定义.定理3(分部求和公式)给定两个复变函数f (s ),g (s ),那么∫a +1z g (s )∇γf (s )d ∇x γ(s )=f (z )g (z )-f (a )g (a )-∫a +1z f (s -1)∇γg (s )d ∇x γ(s ),这里z ,a ∈C ,且假定z -a ∈N .证明应用命题1,可得g (s )∇γf (s )=∇γ[f (z )g (z )]-f (s -1)∇γg (s ),这样就有g (s )∇r f (s )=∇r [f (z )g (z )]-f (s -1)∇r g (s ).关于变量s ,从a +1到z 求和,那么可得∫a +1z g (s )∇γf (s )d ∇x γ(s )=∫a +1z ∇γ[f (z )g (z )]∇x γ(s )-∫a +1z f (s -1)∇γg (s )d ∇x γ(s )=f (z )g (z )-f (a )g (a )-∫a +1z f (s -1)∇γg (s )d ∇x γ(s ).与非一致格子上Riemann-Liouville 型分数阶差分定义的思想来源一样,对于非一致格子上Caputo 型分数阶差分定义思想,也是受启发于非一致格子上广义Abel 方程式(28)的解.在本文第3节,借助于非一致格子上的Euler Beta 公式,我们已经求出广义Abel 方程∇-αγg (z )=f (z ),0<m -1<α≤m ,是g (z )=∇αγf (z )=∇m γ∇-m +αγ+αf (z ).(36)现在我们将用分部求和公式,给出式(36)的另一种新的表达式.事实上,我们有∇a γf (z )=∇m γ∇-m +aγ+a f (z )=∇mγ∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α-1)[Γ(m -α)]qf (s )d ∇x γ+α(s ).(37)应用恒等式∇(s )[x γ+m -1(z )-x γ+m -1(s )](m -α)∇x γ+α(s )=∇(s )[x γ+m -1(z )-x γ+m -1(s -1)](m -α)∇x γ+α(s -1)=-[m -α]q [x γ+m -1(z )-x γ+m -1(s -1)](m -α-1),那么以下表达式程金发:非一致格子上离散分数阶差分与分数阶和分第1期9∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α-1)[Γ(m -α)]qf (s )d ∇x γ+α(s ),可被改写成∫a +1zf (s )∇(s ){-[x γ+m -1(z )-x γ+m -1(s )](m -α)[Γ(m -α+1)]q}d ∇s =∫a +1z f (s )∇γ+α-1{-[x γ+m -1(z )-x γ+m -1(s )](m -α)[Γ(m -α+1)]q}d ∇x γ+α-1(s ).应用分部求和公式,可得∫a +1zf (s )∇γ+α-1{-[x γ+m -1(z )-x γ+m -1(s )](m -α)[Γ(m -α+1)]q}d ∇x γ+α-1(s )=f (a )[x γ+m -1(z )-x γ+m -1(a )](m -α)[Γ(m -α+1)]q+∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α)[Γ(m -α+1)]q∇γ+α-1[f (s )]d ∇x γ+α-1(s ).因此,这可导出∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α-1)[Γ(m -α)]q}f (s )d ∇x γ+α(s )=f (a )[x γ+m -1(z )-x γ+m -1(a )](m -α)[Γ(m -α+1)]q+∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α)[Γ(m -α+1)]q∇γ+α-1[f (s )]d ∇x γ+α-1(s ).(38)进一步,考虑∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α)[Γ(m -α+1)]q∇γ+α-1[f (s )]d ∇x γ+α-1(s ),(39)利用恒等式∇(s )[x γ+m -1(z )-x γ+m -1(s )](m -α+1)∇x γ+α-1(s )=∇(s )[x γ+m -1(z )-x γ+m -1(s -1)](m -α+1)∇x γ+α-1(s -1)=-[m -α+1]q [x γ+m -1(z )-x γ+m -1(s -1)](m -α),表达式(39)能被改写成∫a +1z∇γ+α-1[f (s )]∇(s ){-[x γ+m -1(z )-x γ+m -1(s -1)](m -α+1)[Γ(m -α+2)]q}d ∇s =∫a +1z∇γ+α-1[f (s )]∇γ+α-2{-[x γ+m -1(z )-x γ+m -1(s -1)](m -α+1)[Γ(m -α+2)]q}d ∇x γ+α-2(s ).由分部求和公式,我们有∫a +1z ∇γ+α-1[f (s )]∇γ+α-2{-[x γ+m -1(z )-x γ+m -1(s -1)](m -α+1)[Γ(m -α+2)]q}d ∇x γ+α-2(s )=∇γ+α-1f (a )[x γ+m -1(z )-x γ+m -1(a )](m -α+1)[Γ(m -α+2)]q +∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α+1)[Γ(m -α+2)]q[∇γ+α-2∇γ+α-1]f (s )d ∇x γ+α-2(s )=闽南师范大学学报(自然科学版)2021年10∇γ+α-1f (a )[x γ+m -1(z )-x γ+m -1(a )](m -α+1)[Γ(m -α+2)]q+∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α+1)[Γ(m -α+2)]q∇2γ+α-2f (s )d ∇x γ+α-2(s ).因此,我们得到∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α)[Γ(m -α+1)]q∇γ+α-1[f (s )]d ∇x γ+α-1(s )=∇γ+α-1f (a )[x γ+m -1(z )-x γ+m -1(a )](m -α+1)[Γ(m -α+2)]q+∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α+1)[Γ(m -α+2)]q∇2γ+α-2f (s )d ∇x γ+α-2(s ).(40)同理,用数学归纳法,我们可得∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α+k -1)[Γ(m -α+k )]q∇kγ+α-k [f (s )]d ∇x γ+α-k (s )=∇kγ+α-kf (a )[x γ+m -1(z )-x γ+m -1(a )](m -α+k )[Γ(m -α+k +1)]q+∫a +1z[x γ+m -1(z )-x γ+m -1(s -1)](m -α+k )[Γ(m -α+k +1)]q∇k +1γ+α-(k +1)f (s )d ∇x γ+α-(k +1)(s ).(k =0,1,⋯,m -1)(41)将式(38),(40)和(41)代入式(37),则有∇αγf ()z =∇m γìíîïïf ()a []x γ+m -1()z -x γ+m -1()a ()m -α[]Γ()m -α+1q +∇γ+α-1f ()a []xγ+m -1()z -x γ+m -1()a ()m -α+1[]Γ()m -α+2q+∇kγ+α-kf ()a []x γ+m -1()z -x γ+m -1()a ()m -α+k []Γ()m -α+k +1q+⋯+∇m -1γ+α-()m -1f ()a []x γ+m -1()z -x γ+m -1()a ()2m -α-1[]Γ()2m -αq+üýþïï∫a +1z []xγ+m -1()z -x γ+m -1()s -1()2m -α-1[]Γ()2m -αq∇m γ+α-mf ()s d ∇x γ+α-m ()s =∇m γ{}∑k =0m -1∇kγ+α-kf ()a []x γ+m -1()z -x γ+m -1()a ()m -α+k []Γ()m -α+k +1q+∇α-2m γ+α-m ∇mγ+α-m f ()z =∑k =0m -1∇kγ+α-kf ()a []x γ-1()z -x γ-1()a ()-α+k []Γ()-α+k +1q+∇α-m γ+α-m ∇mγ+α-m f ()z .总之,我们有下面的程金发:非一致格子上离散分数阶差分与分数阶和分第1期11定理4(广义Abel 方程解2)假设定义在数集{}a +1,a +2,⋯,z 上的函数f ()z 和g ()z 满足∇-αγg ()z =f ()z ,0<m -1<Re α≤m ,那么g ()z =∑k =0m -1∇k γ+α-kf ()a []xγ-1()z -x γ-1()a ()-α+k []Γ()-α+k +1q+∇α-m γ+α-m ∇mγ+α-m f ()z .受到定理4的启示,我们很自然地给出函数f ()z 的α-阶()0<m -1<Re α≤m Caputo 分数阶差分如下:定义8(Caputo 分数阶差分)让m 是Re α超过的最小整数,非一致格子上定义在数集{}a +1,a +2,⋯,z 函数f ()z 的α-阶Caputo 分数阶差分定义为C∇αγf ()z =∇α-m γ+α-m ∇mγ+α-m f ()z .最后,本文再强调指出:对于非一致格子上超几何差分方程式(2),在特定条件下存在关于x ()s 多项式形式的解,如果用Rodrigues 公式表示的话,它含有整数阶高阶差分.一个重要的问题是:若该特定条件不满足,那么非一致格子超几何差分方程的解就不存在关于x ()s 的多项式形式,这样高阶整数阶差分将不再起作用了,这就迫切需要我们引入一种非一致格子上分数阶差分的新概念和新理论.因此,关于非一致格子上阶分数阶差分及阶分数阶和分的定义是一个十分有趣和重要的问题.有关非一致格子超几何差分方程与离散分数阶差和分的联系,更深入的内容参见笔者著作[16]及文献[19-21].(42)(43)参考文献:[1]Kilbas A A,Srivastava H M,Trujillo J J.Theory and applications of fractional differential equations[M].Holland:North-Hol-land Mathatics Studies,Elsevier,2006.[2]Miller S,Ross B.An introduction to the fractional calculus and fractional differential equations[M].NewYork:JohnWiley andSons,1993.[3]Podlubny I.Fractional Differential Equations[M].San Diego,CA:Academic Press,1999.[4]Samko S G,Kilbas A A,Marichev O I.Fractional integrals and derivatives:theory and applications[M].London:Gordon andBreach,1993.[5]Anastassiou G A.Nabla discrete fractional calculus and nalba inequalities[J].Mathematical and Computer Modelling,2010,51:562-571.[6]Atici F M,Eloe P W.Discrete fractional calculus with the nable operator[J].Electronic Journal of Qualitative Theory of Differ-ential Equations,Spec.Ed.I,2009(3):1-12.[7]Atici F M,Eloe P W.Initialvalue problems in discrete fractional calulus[J].Pro.Amer.Math.Soc,2009,137:981-989.[8]Ferreira A C,Torres F M.Fractional h-differences arising from the calculus of variations[J].Appl Anal Discrete Math,2011(5):110-121.[9]程金发.分数阶差分方程理论[M].厦门:厦门大学出版社,2011.[10]Goodrich C,Peterson A C.Discrete fractional discrete fractional discrete fractional calculus[M].Switzerland:Springer Inter-national Publishing,2015.[11]Annaby M H,Mansour Z S.q-Fractional Calculus and Equations[M].NewYork:Springer-Verlag,2012.[12]Andrews G E,Askey R,Roy R.Special functions.Encyclopedia of Mathematics and its Applications[M].Cambridge:Cam-bridge University Press,1999.[13]Wang Z X,Guo D R.Special Functions[M].Singapore:World Scientific Publishing,1989.闽南师范大学学报(自然科学版)2021年12[14]Nikiforov A F,Suslov S K,Uvarov V B.Classical orthogonal polynomials of a discrete variable[M].Berlin:Springer-Verlag,1991.[15]Nikiforov A F,Uvarov V B.Special functions of mathematical physics:a unified introduction with applications[M].Basel:Birkhauser Verlag,1988.[16]程金发.非一致格子超几何方程与分数阶差和分[M].北京:科学出版社,2021.[17]Atakishiyev N M,Suslov S K.Difference hypergeometric functions,in:progress in approximation theory[M].New York:Springer-Verlag,1992:1-35.[18]Suslov S K.On the theory of difference analogues of special functions of hypergeo-metric type[J].Russian Math Surveys,1989,44:227-278.[19]Cheng J F,Jia L K.Generalizations of rodrigues type formulas for hypergeometric difference equations on nonuniform[J].Journal of Difference Equations and Applications,2020,26(4):435-457.[20]Cheng J F,Dai W Z.Adjoint difference equation for a Nikiforov-Uvarov-Suslov difference equation of hypergeometric typeon non-uniform Lattices[J].Ramanujan Journal,2020,53:285-318.[21]Cheng J F.On the complex difference equation of hypergeometric type on non-uniform lattices[J].Acta Mathematical Sinica,English Series,2020,36(5):487–511.[责任编辑:钟国翔]程金发:非一致格子上离散分数阶差分与分数阶和分第1期13。
多序列比对介绍多序列比对是一种在生物信息学领域中常用的方法,用于比较多个生物序列之间的相似性和差异性。
通过多序列比对,可以揭示生物序列的结构和功能信息,帮助科学家理解生物进化、基因功能和蛋白质结构等重要问题。
本文将详细介绍多序列比对的原理、方法和应用。
原理多序列比对的基本原理是将多个生物序列进行对齐,找出它们之间的共同模式和差异。
通过比较序列之间的相似性和差异性,可以推断它们的进化关系、功能和结构等信息。
方法多序列比对的方法主要分为两类:全局比对和局部比对。
全局比对是将整个序列进行对齐,适用于序列相似性较高的情况。
常用的全局比对算法包括Needleman-Wunsch算法和Smith-Waterman算法。
局部比对是将序列的一部分进行对齐,适用于序列相似性较低的情况。
常用的局部比对算法包括BLAST和FASTA。
应用多序列比对在生物信息学中有广泛的应用。
以下是一些常见的应用场景:进化分析通过比较不同物种的基因序列,可以推断它们的进化关系和演化过程。
多序列比对可以帮助科学家重建物种的进化树,揭示物种之间的亲缘关系。
基因功能预测通过比较不同基因的序列,可以推断它们的功能和作用机制。
多序列比对可以帮助科学家鉴定基因家族、识别保守区域和预测功能位点。
蛋白质结构预测通过比较不同蛋白质的序列,可以推断它们的结构和功能。
多序列比对可以帮助科学家预测蛋白质的二级结构、三级结构和功能域。
疾病研究通过比较病毒或细菌的基因序列,可以揭示它们的变异和毒力机制。
多序列比对可以帮助科学家研究疾病的起源、传播和治疗。
常用工具多序列比对的计算复杂度较高,因此需要使用专门的软件和工具。
以下是一些常用的多序列比对工具:1.ClustalW:一种经典的多序列比对工具,支持全局比对和局部比对。
2.MAFFT:一种快速而准确的多序列比对工具,适用于大规模序列比对。
3.MUSCLE:一种高效的多序列比对工具,适用于大规模序列比对和高质量比对结果。
丝氨酸衍生方法测hplc英文回答:HPLC (High Performance Liquid Chromatography) is a commonly used analytical technique for the separation and quantification of compounds in a mixture. When it comes to analyzing serine derivatives, there are several methods that can be employed for HPLC analysis.One method for the analysis of serine derivatives by HPLC involves the use of a reverse-phase column. In this method, the mobile phase is typically a mixture of water and organic solvent, such as acetonitrile or methanol. The composition of the mobile phase is optimized to achieve good separation of the serine derivatives. Additionally, the pH of the mobile phase can be adjusted to further improve the separation.Another method for the analysis of serine derivatives by HPLC is the pre-column derivatization technique. Thismethod involves the derivatization of the serinederivatives with a suitable reagent prior to injection onto the HPLC column. The derivatization reaction typically involves the formation of a fluorescent or UV-absorbing derivative, which enhances the detection sensitivity of the serine derivatives.In both methods, it is important to optimize the HPLC conditions, including the composition of the mobile phase, the flow rate, the column temperature, and the detection wavelength. Additionally, the use of a suitable internal standard can aid in the quantification of the serine derivatives.Overall, there are multiple methods available for the HPLC analysis of serine derivatives, and the choice of method depends on the specific requirements of the analysis, such as sensitivity, selectivity, and ease of use.中文回答:HPLC(高效液相色谱)是一种常用的分析技术,用于混合物中化合物的分离和定量。
MATLAB总结一MATLAB常用函数1、特殊变量与常数2、操作符与特殊字符3、基本数学函数4、基本矩阵和矩阵操作5、数值分析和傅立叶变换6、多项式与插值7、绘图函数二Matlab工作间常用命令:1、常用的窗口命令2、有关文件及其操作的语句3、启动与退出的命令4、管理变量工作空间的命令5、对命令窗口控制的常用命令6、此外还有一些常用的命令:↑Ctrl+p 调用上一次的命令↓Ctrl+n 调用下一行的命令←Ctrl+b 退后一格→Ctrl+f 前移一格Ctrl + ←Ctrl+r 向右移一个单词Ctrl + →Ctrl+l 向左移一个单词Home Ctrl+a 光标移到行首End Ctrl+e 光标移到行尾Esc Ctrl+u 清除一行Del Ctrl+d 清除光标后字符Backspace Ctrl+h 清除光标前字符Ctrl+k 清除光标至行尾字 Ctrl+c 中断程序运行三Matlab 运行加速1)性能加速a、采用如下数据类型:logical、char、int、uint、double;b、数据维数不超过3;c、f or循环范围内只采用标量值;只调用内建函数..if 、else if 、while、swicth的条件测试语句只采用标量;d、同一行的命令条数为一条;e、命令操作为改变数据类型或者形状大小;维数;f、复数写为:a+bj型;2遵守3条准则a、避免使用循环语句将循环语句向量化:向量化技术函数有All、diff、ipermute、permute、reshape、squeeze、any、find、logical、prod、shiftdim、sub2ind、cumsum、ind2sub、ndgrid、repmat、sort、sum 等;不得不使用循环语句时;超过2重;循环次数少的在外环;b、预分配矩阵空间函数有:zeros、ones、cell、struct、repmat和采用repmat函数对非double 型预分配空间或对一个变量扩容;c、优先使用内建函数和function;3绝招:采用Mex技术;或者利用matlab提供的工具将程序转化为C语言、Fortran 语言注意:比较向量化和加速器;加速之前采用profiler测试各部分耗时情况..SIMILINK模块库按功能进行分为以下8类子库:Continuous连续模块Discrete离散模块Function&Tables函数和平台模块Math数学模块Nonlinear非线性模块Signals&Systems信号和系统模块Sinks接收器模块Sources输入源模块连续模块Continuouscontinuous.mdlIntegrator:输入信号积分Derivative:输入信号微分State-Space:线性状态空间系统模型Transfer-Fcn:线性传递函数模型Zero-Pole:以零极点表示的传递函数模型Memory:存储上一时刻的状态值Transport Delay:输入信号延时一个固定时间再输出Variable Transport Delay:输入信号延时一个可变时间再输出离散模块Discrete discrete.mdlDiscrete-time Integrator:离散时间积分器Discrete Filter:IIR与FIR滤波器Discrete State-Space:离散状态空间系统模型Discrete Transfer-Fcn:离散传递函数模型Discrete Zero-Pole:以零极点表示的离散传递函数模型First-Order Hold:一阶采样和保持器Zero-Order Hold:零阶采样和保持器Unit Delay:一个采样周期的延时函数和平台模块Function&Tables function.mdlFcn:用自定义的函数表达式进行运算:利用matlab的现有函数进行运算S-Function:调用自编的S函数的程序进行运算Look-Up Table:建立输入信号的查询表线性峰值匹配Look-Up Table2-D:建立两个输入信号的查询表线性峰值匹配数学模块Math math.mdlSum:加减运算Product:乘运算Dot Product:点乘运算Gain:比例运算Math Function:包括指数函数、对数函数、求平方、开根号等常用数学函数Trigonometric Function:三角函数;包括正弦、余弦、正切等MinMax:最值运算Abs:取绝对值Sign:符号函数Logical Operator:逻辑运算Relational Operator:关系运算Complex to Magnitude-Angle:由复数输入转为幅值和相角输出Magnitude-Angle to Complex:由幅值和相角输入合成复数输出Complex to Real-Imag:由复数输入转为实部和虚部输出Real-Imag to Complex:由实部和虚部输入合成复数输出非线性模块Nonlinear nonlinear.mdlSaturation:饱和输出;让输出超过某一值时能够饱和..Relay:滞环比较器;限制输出值在某一范围内变化..Switch:开关选择;当第二个输入端大于临界值时;输出由第一个输入端而来;否则输出由第三个输入端而来..Manual Switch:手动选择开关信号和系统模块Signal&Systems sigsys.mdlIn1:输入端..Out1:输出端..Mux:将多个单一输入转化为一个复合输出..Demux:将一个复合输入转化为多个单一输出..Ground:连接到没有连接到的输入端..Terminator:连接到没有连接到的输出端..SubSystem:建立新的封装Mask功能模块接收器模块Sinks sinks.mdlScope:示波器..XY Graph:显示二维图形..To Workspace:将输出写入MA TLAB的工作空间..To File.mat:将输出写入数据文件..输入源模块Sources sources.mdlConstant:常数信号..Clock:时钟信号..From Workspace:来自MA TLAB的工作空间..From File.mat:来自数据文件..Pulse Generator:脉冲发生器..Repeating Sequence:重复信号..Signal Generator:信号发生器;可以产生正弦、方波、锯齿波及随意波..Sine Wave:正弦波信号..Step:阶跃波信号..在MA TLAB命令窗口下直接运行一个已经存在的simulink模型t;x;y=sim'model';timespan;option;ut其中;t为返回的仿真时间向量;x为返回的状态矩阵;y为返回的输出矩阵;model为系统Simulink模型文件名;timespan为仿真时间; option为仿真参数选择项;由simset设置; ut 为选择外部产生输入;ut=T;u1;u2;...;un..Sources库信号源库无输入;至少一个输出Sine Wave: 产生幅值、频率可设置的正弦波信号..Step: 产生幅值、阶跃时间可设置的阶跃信号..Sinks库显示和写模块输出Display: 数字表;显示指定模块的输出数值XY Graph: 用同一图形窗口;显示X-Y坐标的图形需现在参数对话框中设置每个坐标的变化范围..Scope: 示波器..显示在仿真过程中产生的信号波形..Continuous库包含描述线性函数的模块Derivative: 微分环节..其输出为其输入信号的微分..Integrator: 积分环节..其输出为其输入信号的积分..Transfer Fcn: 分子分母为多项式形式的传递函数Zero-Poles: 零极点增益形式的传递函数..Math库包含描述一般数学函数的模块..AddSign: 符号函数..输出为输入信号的符号Math function: 实现一个数学函数..Signals & Systems 库Demux: 信号分路器..将混路器输出的信号依照原来的构成方法分解成多路信号..Mux: 信号汇总器..将多路信号依照向量的形式混合成一路信号..Simulink环境下的仿真运行仿真参数对话框Solver页设置仿真开始和终止时间Solver options仿真算法选择:分为定步长算法和变步长算法离散系统一般默认选择定步长算法;在实时控制中则必须选用定步长算法变步长算法;对连续系统仿真一般选择ode45;步长范围用auto Error Tolerance误差限度:算法的误差是指当前状态值与当前状态估计值的误差;分为Relative tolerance相对限度和Absolute tolerance绝对限度;通常可选auto..。
稀疏阵列的鲁棒矩阵填充DOA估计算法
张芸萌;董玫;陈伯孝
【期刊名称】《系统工程与电子技术》
【年(卷),期】2024(46)5
【摘要】稀疏阵列布阵灵活,增大阵列孔径的同时还能减少阵元间耦合,但基于稀疏阵列的传统波达方向估计会导致角度模糊混叠,带来估计精度差和稳健性不足的问题。
针对以上问题,提出一种适用于稀疏阵列波达方向估计的加权截断奇异值投影(weighted truncated singular value projection,WT-SVP)的鲁棒矩阵填充算法。
在填充迭代过程中根据奇异值的大小分配权重,突出大奇异值包含的阵列信息,减少
小奇异值中不必要的噪声信息,从而优化传统奇异值投影算法。
该算法可以实现稀
疏阵列的孔洞信息恢复,对不连续阵元充分利用,同时WT-SVP填充算法实现了稀疏阵列波达方向估计的高精度、高分辨以及在低信噪比、低快拍时的高鲁棒性。
【总页数】7页(P1477-1483)
【作者】张芸萌;董玫;陈伯孝
【作者单位】西安电子科技大学雷达信号处理全国重点实验室
【正文语种】中文
【中图分类】TN953
【相关文献】
1.基于矩阵填充的互质阵列欠定DOA估计方法
2.一种基于天线阵列预处理盲多用户检测算法及其对DOA估计误差的鲁棒性研究
3.基于 AVS 和稀疏表示的鲁棒语
者声源 DOA 估计方法4.一种基于平行稀疏阵列虚拟孔洞填充的二维DOA估计算法
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一、字母顺序表 (1)二、常用的数学英语表述 (7)三、代数英语(高端) (13)一、字母顺序表1、数学专业词汇Aabsolute value 绝对值 accept 接受 acceptable region 接受域additivity 可加性 adjusted 调整的 alternative hypothesis 对立假设analysis 分析 analysis of covariance 协方差分析 analysis of variance 方差分析 arithmetic mean 算术平均值 association 相关性 assumption 假设 assumption checking 假设检验availability 有效度average 均值Bbalanced 平衡的 band 带宽 bar chart 条形图beta-distribution 贝塔分布 between groups 组间的 bias 偏倚 binomial distribution 二项分布 binomial test 二项检验Ccalculate 计算 case 个案 category 类别 center of gravity 重心 central tendency 中心趋势 chi-square distribution 卡方分布 chi-square test 卡方检验 classify 分类cluster analysis 聚类分析 coefficient 系数 coefficient of correlation 相关系数collinearity 共线性 column 列 compare 比较 comparison 对照 components 构成,分量compound 复合的 confidence interval 置信区间 consistency 一致性 constant 常数continuous variable 连续变量 control charts 控制图 correlation 相关 covariance 协方差 covariance matrix 协方差矩阵 critical point 临界点critical value 临界值crosstab 列联表cubic 三次的,立方的 cubic term 三次项 cumulative distribution function 累加分布函数 curve estimation 曲线估计Ddata 数据default 默认的definition 定义deleted residual 剔除残差density function 密度函数dependent variable 因变量description 描述design of experiment 试验设计 deviations 差异 df.(degree of freedom) 自由度 diagnostic 诊断dimension 维discrete variable 离散变量discriminant function 判别函数discriminatory analysis 判别分析distance 距离distribution 分布D-optimal design D-优化设计Eeaqual 相等 effects of interaction 交互效应 efficiency 有效性eigenvalue 特征值equal size 等含量equation 方程error 误差estimate 估计estimation of parameters 参数估计estimations 估计量evaluate 衡量exact value 精确值expectation 期望expected value 期望值exponential 指数的exponential distributon 指数分布 extreme value 极值F factor 因素,因子 factor analysis 因子分析 factor score 因子得分 factorial designs 析因设计factorial experiment 析因试验fit 拟合fitted line 拟合线fitted value 拟合值 fixed model 固定模型 fixed variable 固定变量 fractional factorial design 部分析因设计 frequency 频数 F-test F检验 full factorial design 完全析因设计function 函数Ggamma distribution 伽玛分布 geometric mean 几何均值 group 组Hharmomic mean 调和均值 heterogeneity 不齐性histogram 直方图 homogeneity 齐性homogeneity of variance 方差齐性 hypothesis 假设 hypothesis test 假设检验Iindependence 独立 independent variable 自变量independent-samples 独立样本 index 指数 index of correlation 相关指数 interaction 交互作用 interclass correlation 组内相关 interval estimate 区间估计 intraclass correlation 组间相关 inverse 倒数的iterate 迭代Kkernal 核 Kolmogorov-Smirnov test柯尔莫哥洛夫-斯米诺夫检验 kurtosis 峰度Llarge sample problem 大样本问题 layer 层least-significant difference 最小显著差数 least-square estimation 最小二乘估计 least-square method 最小二乘法 level 水平 level of significance 显著性水平 leverage value 中心化杠杆值 life 寿命 life test 寿命试验 likelihood function 似然函数 likelihood ratio test 似然比检验linear 线性的 linear estimator 线性估计linear model 线性模型 linear regression 线性回归linear relation 线性关系linear term 线性项logarithmic 对数的logarithms 对数 logistic 逻辑的 lost function 损失函数Mmain effect 主效应 matrix 矩阵 maximum 最大值 maximum likelihood estimation 极大似然估计 mean squared deviation(MSD) 均方差 mean sum of square 均方和 measure 衡量 media 中位数 M-estimator M估计minimum 最小值 missing values 缺失值 mixed model 混合模型 mode 众数model 模型Monte Carle method 蒙特卡罗法 moving average 移动平均值multicollinearity 多元共线性multiple comparison 多重比较 multiple correlation 多重相关multiple correlation coefficient 复相关系数multiple correlation coefficient 多元相关系数 multiple regression analysis 多元回归分析multiple regression equation 多元回归方程 multiple response 多响应 multivariate analysis 多元分析Nnegative relationship 负相关 nonadditively 不可加性 nonlinear 非线性 nonlinear regression 非线性回归 noparametric tests 非参数检验 normal distribution 正态分布null hypothesis 零假设 number of cases 个案数Oone-sample 单样本 one-tailed test 单侧检验 one-way ANOVA 单向方差分析 one-way classification 单向分类 optimal 优化的optimum allocation 最优配制 order 排序order statistics 次序统计量 origin 原点orthogonal 正交的 outliers 异常值Ppaired observations 成对观测数据paired-sample 成对样本parameter 参数parameter estimation 参数估计 partial correlation 偏相关partial correlation coefficient 偏相关系数 partial regression coefficient 偏回归系数 percent 百分数percentiles 百分位数 pie chart 饼图 point estimate 点估计 poisson distribution 泊松分布polynomial curve 多项式曲线polynomial regression 多项式回归polynomials 多项式positive relationship 正相关 power 幂P-P plot P-P概率图predict 预测predicted value 预测值prediction intervals 预测区间principal component analysis 主成分分析 proability 概率 probability density function 概率密度函数 probit analysis 概率分析 proportion 比例Qqadratic 二次的 Q-Q plot Q-Q概率图 quadratic term 二次项 quality control 质量控制 quantitative 数量的,度量的 quartiles 四分位数Rrandom 随机的 random number 随机数 random number 随机数 random sampling 随机取样random seed 随机数种子 random variable 随机变量 randomization 随机化 range 极差rank 秩 rank correlation 秩相关 rank statistic 秩统计量 regression analysis 回归分析regression coefficient 回归系数regression line 回归线reject 拒绝rejection region 拒绝域 relationship 关系 reliability 可*性 repeated 重复的report 报告,报表 residual 残差 residual sum of squares 剩余平方和 response 响应risk function 风险函数 robustness 稳健性 root mean square 标准差 row 行 run 游程run test 游程检验Sample 样本 sample size 样本容量 sample space 样本空间 sampling 取样 sampling inspection 抽样检验 scatter chart 散点图 S-curve S形曲线 separately 单独地 sets 集合sign test 符号检验significance 显著性significance level 显著性水平significance testing 显著性检验 significant 显著的,有效的 significant digits 有效数字 skewed distribution 偏态分布 skewness 偏度 small sample problem 小样本问题 smooth 平滑 sort 排序 soruces of variation 方差来源 space 空间 spread 扩展square 平方 standard deviation 标准离差 standard error of mean 均值的标准误差standardization 标准化 standardize 标准化 statistic 统计量 statistical quality control 统计质量控制 std. residual 标准残差 stepwise regression analysis 逐步回归 stimulus 刺激 strong assumption 强假设 stud. deleted residual 学生化剔除残差stud. residual 学生化残差 subsamples 次级样本 sufficient statistic 充分统计量sum 和 sum of squares 平方和 summary 概括,综述Ttable 表t-distribution t分布test 检验test criterion 检验判据test for linearity 线性检验 test of goodness of fit 拟合优度检验 test of homogeneity 齐性检验 test of independence 独立性检验 test rules 检验法则 test statistics 检验统计量 testing function 检验函数 time series 时间序列 tolerance limits 容许限total 总共,和 transformation 转换 treatment 处理 trimmed mean 截尾均值 true value 真值 t-test t检验 two-tailed test 双侧检验Uunbalanced 不平衡的 unbiased estimation 无偏估计 unbiasedness 无偏性 uniform distribution 均匀分布Vvalue of estimator 估计值 variable 变量 variance 方差 variance components 方差分量 variance ratio 方差比 various 不同的 vector 向量Wweight 加权,权重 weighted average 加权平均值 within groups 组内的ZZ score Z分数2. 最优化方法词汇英汉对照表Aactive constraint 活动约束 active set method 活动集法 analytic gradient 解析梯度approximate 近似 arbitrary 强制性的 argument 变量 attainment factor 达到因子Bbandwidth 带宽 be equivalent to 等价于 best-fit 最佳拟合 bound 边界Ccoefficient 系数 complex-value 复数值 component 分量 constant 常数 constrained 有约束的constraint 约束constraint function 约束函数continuous 连续的converge 收敛 cubic polynomial interpolation method三次多项式插值法 curve-fitting 曲线拟合Ddata-fitting 数据拟合 default 默认的,默认的 define 定义 diagonal 对角的 direct search method 直接搜索法 direction of search 搜索方向 discontinuous 不连续Eeigenvalue 特征值 empty matrix 空矩阵 equality 等式 exceeded 溢出的Ffeasible 可行的 feasible solution 可行解 finite-difference 有限差分 first-order 一阶GGauss-Newton method 高斯-牛顿法 goal attainment problem 目标达到问题 gradient 梯度 gradient method 梯度法Hhandle 句柄 Hessian matrix 海色矩阵Independent variables 独立变量inequality 不等式infeasibility 不可行性infeasible 不可行的initial feasible solution 初始可行解initialize 初始化inverse 逆 invoke 激活 iteration 迭代 iteration 迭代JJacobian 雅可比矩阵LLagrange multiplier 拉格朗日乘子 large-scale 大型的 least square 最小二乘 least squares sense 最小二乘意义上的 Levenberg-Marquardt method 列文伯格-马夸尔特法line search 一维搜索 linear 线性的 linear equality constraints 线性等式约束linear programming problem 线性规划问题 local solution 局部解M medium-scale 中型的 minimize 最小化 mixed quadratic and cubic polynomialinterpolation and extrapolation method 混合二次、三次多项式内插、外插法multiobjective 多目标的Nnonlinear 非线性的 norm 范数Oobjective function 目标函数 observed data 测量数据 optimization routine 优化过程optimize 优化 optimizer 求解器 over-determined system 超定系统Pparameter 参数 partial derivatives 偏导数 polynomial interpolation method 多项式插值法Qquadratic 二次的 quadratic interpolation method 二次内插法 quadratic programming 二次规划Rreal-value 实数值 residuals 残差 robust 稳健的 robustness 稳健性,鲁棒性S scalar 标量 semi-infinitely problem 半无限问题 Sequential Quadratic Programming method 序列二次规划法 simplex search method 单纯形法 solution 解 sparse matrix 稀疏矩阵 sparsity pattern 稀疏模式 sparsity structure 稀疏结构 starting point 初始点 step length 步长 subspace trust region method 子空间置信域法 sum-of-squares 平方和 symmetric matrix 对称矩阵Ttermination message 终止信息 termination tolerance 终止容限 the exit condition 退出条件 the method of steepest descent 最速下降法 transpose 转置Uunconstrained 无约束的 under-determined system 负定系统Vvariable 变量 vector 矢量Wweighting matrix 加权矩阵3 样条词汇英汉对照表Aapproximation 逼近 array 数组 a spline in b-form/b-spline b样条 a spline of polynomial piece /ppform spline 分段多项式样条Bbivariate spline function 二元样条函数 break/breaks 断点Ccoefficient/coefficients 系数cubic interpolation 三次插值/三次内插cubic polynomial 三次多项式 cubic smoothing spline 三次平滑样条 cubic spline 三次样条cubic spline interpolation 三次样条插值/三次样条内插 curve 曲线Ddegree of freedom 自由度 dimension 维数Eend conditions 约束条件 input argument 输入参数 interpolation 插值/内插 interval取值区间Kknot/knots 节点Lleast-squares approximation 最小二乘拟合Mmultiplicity 重次 multivariate function 多元函数Ooptional argument 可选参数 order 阶次 output argument 输出参数P point/points 数据点Rrational spline 有理样条 rounding error 舍入误差(相对误差)Sscalar 标量 sequence 数列(数组) spline 样条 spline approximation 样条逼近/样条拟合spline function 样条函数 spline curve 样条曲线 spline interpolation 样条插值/样条内插 spline surface 样条曲面 smoothing spline 平滑样条Ttolerance 允许精度Uunivariate function 一元函数Vvector 向量Wweight/weights 权重4 偏微分方程数值解词汇英汉对照表Aabsolute error 绝对误差 absolute tolerance 绝对容限 adaptive mesh 适应性网格Bboundary condition 边界条件Ccontour plot 等值线图 converge 收敛 coordinate 坐标系Ddecomposed 分解的 decomposed geometry matrix 分解几何矩阵 diagonal matrix 对角矩阵 Dirichlet boundary conditions Dirichlet边界条件Eeigenvalue 特征值 elliptic 椭圆形的 error estimate 误差估计 exact solution 精确解Ggeneralized Neumann boundary condition 推广的Neumann边界条件 geometry 几何形状geometry description matrix 几何描述矩阵 geometry matrix 几何矩阵 graphical user interface(GUI)图形用户界面Hhyperbolic 双曲线的Iinitial mesh 初始网格Jjiggle 微调LLagrange multipliers 拉格朗日乘子Laplace equation 拉普拉斯方程linear interpolation 线性插值 loop 循环Mmachine precision 机器精度 mixed boundary condition 混合边界条件NNeuman boundary condition Neuman边界条件 node point 节点 nonlinear solver 非线性求解器 normal vector 法向量PParabolic 抛物线型的 partial differential equation 偏微分方程 plane strain 平面应变 plane stress 平面应力 Poisson's equation 泊松方程 polygon 多边形 positive definite 正定Qquality 质量Rrefined triangular mesh 加密的三角形网格 relative tolerance 相对容限 relative tolerance 相对容限 residual 残差 residual norm 残差范数Ssingular 奇异的二、常用的数学英语表述1.Logic∃there exist∀for allp⇒q p implies q / if p, then qp⇔q p if and only if q /p is equivalent to q / p and q are equivalent2.Setsx∈A x belongs to A / x is an element (or a member) of Ax∉A x does not belong to A / x is not an element (or a member) of AA⊂B A is contained in B / A is a subset of BA⊃B A contains B / B is a subset of AA∩B A cap B / A meet B / A intersection BA∪B A cup B / A join B / A union BA\B A minus B / the diference between A and BA×B A cross B / the cartesian product of A and B3. Real numbersx+1 x plus onex-1 x minus onex±1 x plus or minus onexy xy / x multiplied by y(x - y)(x + y) x minus y, x plus yx y x over y= the equals signx = 5 x equals 5 / x is equal to 5x≠5x (is) not equal to 5x≡y x is equivalent to (or identical with) yx ≡ y x is not equivalent to (or identical with) yx > y x is greater than yx≥y x is greater than or equal to yx < y x is less than yx≤y x is less than or equal to y0 < x < 1 zero is less than x is less than 10≤x≤1zero is less than or equal to x is less than or equal to 1| x | mod x / modulus xx 2 x squared / x (raised) to the power 2x 3 x cubedx 4 x to the fourth / x to the power fourx n x to the nth / x to the power nx −n x to the (power) minus nx (square) root x / the square root of xx 3 cube root (of) xx 4 fourth root (of) xx n nth root (of) x( x+y ) 2 x plus y all squared( x y ) 2 x over y all squaredn! n factorialx ^ x hatx ¯ x barx ˜x tildex i xi / x subscript i / x suffix i / x sub i∑ i=1 n a i the sum from i equals one to n a i / the sum as i runs from 1 to n of the a i4. Linear algebra‖ x ‖the norm (or modulus) of xOA →OA / vector OAOA ¯ OA / the length of the segment OAA T A transpose / the transpose of AA −1 A inverse / the inverse of A5. Functionsf( x ) fx / f of x / the function f of xf:S→T a function f from S to Tx→y x maps to y / x is sent (or mapped) to yf'( x ) f prime x / f dash x / the (first) derivative of f with respect to xf''( x ) f double-prime x / f double-dash x / the second derivative of f with r espect to xf'''( x ) triple-prime x / f triple-dash x / the third derivative of f with respect to xf (4) ( x ) f four x / the fourth derivative of f with respect to x∂f ∂ x 1the partial (derivative) of f with respect to x1∂ 2 f ∂ x 1 2the second partial (derivative) of f with respect to x1∫ 0 ∞the integral from zero to infinitylimx→0 the limit as x approaches zerolimx→0 + the limit as x approaches zero from abovelimx→0 −the limit as x approaches zero from belowlog e y log y to the base e / log to the base e of y / natural log (of) ylny log y to the base e / log to the base e of y / natural log (of) y一般词汇数学mathematics, maths(BrE), math(AmE)公理axiom定理theorem计算calculation运算operation证明prove假设hypothesis, hypotheses(pl.)命题proposition算术arithmetic加plus(prep.), add(v.), addition(n.)被加数augend, summand加数addend和sum减minus(prep.), subtract(v.), subtraction(n.)被减数minuend减数subtrahend差remainder乘times(prep.), multiply(v.), multiplication(n.)被乘数multiplicand, faciend乘数multiplicator积product除divided by(prep.), divide(v.), division(n.)被除数dividend除数divisor商quotient等于equals, is equal to, is equivalent to 大于is greater than小于is lesser than大于等于is equal or greater than小于等于is equal or lesser than运算符operator数字digit数number自然数natural number整数integer小数decimal小数点decimal point分数fraction分子numerator分母denominator比ratio正positive负negative零null, zero, nought, nil十进制decimal system二进制binary system十六进制hexadecimal system权weight, significance进位carry截尾truncation四舍五入round下舍入round down上舍入round up有效数字significant digit无效数字insignificant digit代数algebra公式formula, formulae(pl.)单项式monomial多项式polynomial, multinomial系数coefficient未知数unknown, x-factor, y-factor, z-factor 等式,方程式equation一次方程simple equation二次方程quadratic equation三次方程cubic equation四次方程quartic equation不等式inequation阶乘factorial对数logarithm指数,幂exponent乘方power二次方,平方square三次方,立方cube四次方the power of four, the fourth power n次方the power of n, the nth power开方evolution, extraction二次方根,平方根square root三次方根,立方根cube root四次方根the root of four, the fourth root n次方根the root of n, the nth root集合aggregate元素element空集void子集subset交集intersection并集union补集complement映射mapping函数function定义域domain, field of definition值域range常量constant变量variable单调性monotonicity奇偶性parity周期性periodicity图象image数列,级数series微积分calculus微分differential导数derivative极限limit无穷大infinite(a.) infinity(n.)无穷小infinitesimal积分integral定积分definite integral不定积分indefinite integral有理数rational number无理数irrational number实数real number虚数imaginary number复数complex number矩阵matrix行列式determinant几何geometry点point线line面plane体solid线段segment射线radial平行parallel相交intersect角angle角度degree弧度radian锐角acute angle直角right angle钝角obtuse angle平角straight angle周角perigon底base边side高height三角形triangle锐角三角形acute triangle直角三角形right triangle直角边leg斜边hypotenuse勾股定理Pythagorean theorem钝角三角形obtuse triangle不等边三角形scalene triangle等腰三角形isosceles triangle等边三角形equilateral triangle四边形quadrilateral平行四边形parallelogram矩形rectangle长length宽width附:在一个分数里,分子或分母或两者均含有分数。
Finite Fields and Their Applications12(2006)26–37/locate/ffa Enumerating permutation polynomials overfinitefields by degree IISergei Konyagin a,Francesco Pappalardi b,∗a Department of Mechanics and Mathematics,Moscow State University,Vorobjovy Gory,119992Moscow,Russiab Dipartimento di Matematica,UniversitàDegli Studi Roma Tre,Largo S.L.Murialdo,1,I-00146Roma,ItalyReceived21July2003;revised31August2004Communicated by Gerhard TurnwaldAvailable online5February2005AbstractThis note is a continuation of a paper by the same authors that appeared in2002in the same journal.First we extend the method of the previous paper proving an asymptotic formula for the number of permutations for which the associated permutation polynomial has d coefficients in specifiedfixed positions equal to0.This also applies to the function N q,d that counts the number of permutations for which the associated permutation polynomial has degree<q−d−1. Next we adopt a more precise approach to show that the asymptotic formula N q,d∼q!/q d holds for d q and =0.03983.©2005Elsevier Inc.All rights reserved.Keywords:Permutation polynomials;Finitefields;Exponential sumsCorresponding author.Fax:+390654888080.E-mail addresses:konyagin@ok.ru(S.Konyagin),pappa@mat.uniroma3.it(F.Pappalardi).1071-5797/$-see front matter©2005Elsevier Inc.All rights reserved.doi:10.1016/j.ffa.2004.12.006S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–3727 1.IntroductionLet F q be afinitefield with q>2elements.For any permutation of the elements of F q the permutation polynomialf (x)=c∈F q (c)1−(x−c)q−1∈F q[x](1)has the property that f (a)= (a)for every a∈F q.From the definition,it follows that for every ,the degree*(f ) q−2.In[2]the authors proved that if S(F q)denotes the group of permutations on F q, then#∈S(F q)|*(f )<q−2−(q−1)!2eq q.A similar result has also been proved by Das[1].Here we consider a more general function.Fix d integers k1,k2,...,k d with the property that0<k1<···<k d<q−1and defineN q(k1,...,k d)=#∈S(F q)|∀i=1,...,d,the k i-th coefficient of f is0,where by k-th coefficient of a polynomial f we mean its coefficient of x k. We will extend the method of[2]proving the following:Theorem1.Nq(k1,...,k d)−q!q d<1+1eq((q−k1−1)q)q/2.The above result also applies toN q,d=#∈S(F q)|*(f )<q−d−1sinceN q,d=N q(q−d−1,...,q−3,q−2). We also haveCorollary1.If d qlog q12log log q−log log log q,then N q,d∼q!/q d(q→∞).28S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–37In the case when d is larger with respect to q,the above statement for N q,d can be improved.In Section3we will prove:Theorem2.Suppose =(e−2)/3e=0.08808···,and d< q.ThenN q,d−q!q d<2d dq2+q−dqd2dq−d(q−d)/2.Therefore we haveCorollary2.The asymptotic formula N q,d∼q!/q d holds for q→∞,d 0q and 0=0.03983is a suitable constant.2.The method of[2]—Proof of Theorem1The coefficient of x i in f (x)in(1)equals(−1)q−iq−1ic∈F qc q−i−1 (c)for i>0.Observe thatq−1i=(−1)i for i=1,...,q−1(the equality is consideredin F q;see[3,Exercise7.1]).Hence the j th coefficient of f (x)is0if and only ifc∈F qc q−j−1 (c)=0.We will follow the proof in[2]that uses exponential sums defining auxiliary functions for each S⊆F q:n S(k1,...,k d)=#⎧⎨⎩ff:F q−→S andc∈F qc q−k i−1f(c)=0,for i=1,...,d⎫⎬⎭.By inclusion–exclusion,it is easy to check thatN q(k1,...,k d)=S⊆F q(−1)q−|S|n S(k1,...,k d).(2)S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–3729 Now we need to evaluate n S(k1,...,k d).If e p(u)=e2 iu p and Tr( )∈F p denotes the trace of ∈F q,thenn S(k1,...,k d)=1q d(a1,...,a d)∈F d qf:F q−→Se p⎛⎝c∈F qTrf(c)di=1a i c q−k i−1⎞⎠=1q(a1,...,a d)∈F d qc∈F qt∈Se pTrtdi=1a i c q−k i−1=|S|qq d+R S,(3)where|R S| q d−1q dmax(a1,...,a d)∈F d q\{0}c∈F qt∈Se pTrtdi=1a i c q−k i−1.Furthermore,since the geometric mean is always bounded by the arithmetic mean,c∈F qt∈Se pTrtdi=1a i c q−k i−1⎛⎝1qc∈F qt∈Se pTrtdi=1a i c q−k i−12⎞⎠q/2⎛⎝1qu∈F q(q−k1−1)t∈Se p(Tr(tu))2⎞⎠q/2.By the identityu∈F qt∈Se p(Tr(tu))2=q|S|(4)we getc∈F qt∈Se pTrtdi=1a i c q−k i−1((q−k1−1)|S|)q/2.30S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–37 Using inclusion–exclusion for counting the mappings F q→F q we see thatS⊆F q(−1)q−|S||S|q=q!.(5)Now let us plug the estimate for|R S|in Eq.(3)and then in Eq.(2).By(5)we obtainNq(k1,...,k d)−q!q d=N q(k1,...,k d)−S⊆F q(−1)q−|S|q d|S|qqd−1q dS⊆F q((q−k1−1)|S|)q/2.Next,using the inequality1+x e x(6) we getNq(k1,...,k d)−q!q d<(q−k1−1)q/2qu=0u q/2qu(q−k1−1)q/2qu=0quq exp−q−uqq/2 =((q−k1−1)q)q/2qu=0qu1eq−u=((q−k1−1)q)q/21+1eq.This completes the proof.Proof of Corollary1.From Theorem1since N q,d=N q(q−d−1,q−d,...,q−2), we haveN q,d−q!q d<2q(dq)q/2.S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–3731 By the Stirling formulalim q→∞q!qeq√2 q=1,(7)we obtain that the error term is dominated by the main term if(dq)q/22q=o1q dqeq√q(q→∞).The above is satisfied ifd q =o1q(q→∞).(8)Replace d=u·q log log qlog qand obtain that(8)holds foru 12−log log log qlog log qand q large enough.3.Proof of Theorem2The key ingredient is to use the special properties of N q,d to estimate(3)more accurately.Assume that d is a positive integer and d<q.Let F q(d)denote the vector space of polynomials with degree up to d with constant term equal to0.If P(x)∈F q(d),then define(P)=minT⊂F q,|T|=d|P(T)|.In view of identity(5)and from(2)and(3)we can write:N q,d−q!q d=N q,d−S⊆F q(−1)q−|S||S|qq d=1qS⊆F q(−1)q−|S|P∈F q(d)\{0}c∈F qt∈Se p(Tr(tP(c)))32S.Konyagin,F .Pappalardi /Finite Fields and Their Applications 12(2006)26–37=1qdS ⊆F q(−1)q −|S |dP ∈F q (d)\{0} (P )=c ∈F qt ∈Se p (Tr (tP (c))).(9)In order to estimate the above we will need some lemmas.Lemma 1.Given an integer ∈N ,let H be the set of polynomials P ∈F q (d)such that (P )= .Then|H | dq qd.Proof.Let us fix one of the q dsubsets of F q with d elements and denote it with T .It is sufficient to show that the number of polynomials P ∈F q (d)with |P (T )|= is at most d q.There are qchoices for the set U =P (T ).Take an arbitrary a ∈F q \T and observe that the polynomials of degree d correspond to functions T ∪{a }→F q .Therefore,the number of polynomials P of degree d such that P (T )⊂U is equal to q d and the number of polynomials P of degree d such that |P (T )|= is atmost q d q.Finally,observe that |P (T )|does not change if we add a constant to any polynomial P .Thus the number of polynomials P ∈F q (d)such that |P (T )|= is at most d qas required.Lemma 2.Assume that d q/3and S ⊆F q .If P ∈F q (d)is such that (P ) 2,thenc ∈F q t ∈Se p (Tr (tP (c))) q 2 (q +d)/2 d −1q q −d (q −d)/2while if (P )=1,P =0,thenc ∈F q t ∈Se p (Tr (tP (c))) q 2 (q +d)/2 dq q −d (q −d)/2Proof.Assume (P ) and writeP (F q )={u 1,u 2,...}S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–3733 where the order is chosen in such a way that#P−1(u1) #P−1(u2) ···.Note that since (P) ,#P−1(u1)+···+#P−1(u −1)<d.(10) Therefore for j= −1, ,...we have#P−1(u j)<dprovided that 2.By(10),there is a set T⊂F q such that|T|=d andT⊃{t:P(t)∈{u1,...,u −1}}.Hence,for each c∈T we have that P(c)∈{u ,u +1,...}.Therefore there are at most d/( −1)elements c of F q with P(c )=P(c).If =1then we use the observation that there are at most d elements c with this property.Denoting =max( ,2),we see that there are at most d/( −1)elements c of F q with P(c )=P(c).Now,bounding from the above the geometric mean with the arithmetic mean,we deducec∈F qt∈Se p(Tr(tP(c)))=c∈Tt∈Se p(Tr(tP(c)))×c∈F q\Tt∈Se p(Tr(tP(c)))|S|d⎛⎝1q−dc∈F q\Tt∈Se p(Tr(tP(c)))2⎞⎠(q−d)/2|S|d⎛⎝1q−dd−1u∈F qt∈Se p(Tr(tu))2⎞⎠(q−d)/2=|S|d1q−dd−1q|S|(q−d)/2,where we used once again(4).This concludes the proof of the lemma in the case |S| q/2.34S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–37If|S|>q/2then,by the identityt∈F qe p(Tr(tP(c)))=0for P(c)=0,we can reduce the product for S to the product for F q\S.Also, the identity combined with the supposition d<q shows that the product is zero for |S|=q.Thus,we can consider q/2<|S|<q.If is the number of the zeros of the polynomial P in F q then we havec∈F qt∈Se p(Tr(tP(c)))=|S|q−|S|c∈F qt∈F q\Se p(Tr(tP(c))).Therefore,c∈F qt∈Se p(Tr(tP(c)))|S|q−|S|d(q−|S|)d1q−dd−1q(q−|S|)(q−d)/2=(|S|(q−|S|))d(q−|S|)(q−3d)/21q−dd−1q(q−d)/2q24dq2(q−3d)/2 1q−dd−1q(q−d)/2,as required.We have taken into account that(q−3d)/2 0by our supposition.The proof of the lemma is complete.Lemma3.If d<e−23eq then we have the following estimate:d =21−1(q−d)/2·qd 2d−1(d−1)q2.Proof.The lemma is trivial for d=1.Consider d>1and define the functionf( )=1−1(q−d)/2·qd.Let d=uq,u<e−23e.Taking into account(6),we havef( +1)/f( )=−1(q−d)/2+1dq+1q−1S.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–3735<−1(1−3u)q/2q<exp−(1−3u)q2q.By our supposition,(1−3u)/2>1/e.Denoting v=q/ we havef( +1)/f( )<ve−v/e.But,by(6),ve−v/e 1for any v.Therefore,f( +1)/f( )<1.Thus,the function f( )is decreasing for2 d.Hence,d =21−1(q−d)/2·qd=d=2f( ) (d−1)f(2).This implies the statement of Lemma3. We are in the condition to prove Theorem2.Proof of Theorem2.In view of Lemma2(recall that d<e−23e q)and Lemma1,theabsolute value of the right-hand side of(9)isS⊆F q1q dqdq2(q+d)/2 dqq−d(q−d)/2⎛⎝q+d=21−1(q−d)/2·qd⎞⎠.By Lemma3,we obtain that the above is<2qq dqdq2(q+d)/2 dqq−d(q−d)/22d dq2=2d dq2+q−d qd2dq−d(q−d)/2.(11)This concludes the proof by using(9).4.Range of uniformity(proof of Corollary2)In this last section we want to establish how large d can be in order for the asymptotic formula N q,d∼q!/q d to hold.36S.Konyagin,F .Pappalardi /Finite Fields and Their Applications 12(2006)26–37Since 0< ,we can use Theorem 2.Substitute d = q ( 0)in (11)and note that in order to have an asymptotic formula it is enough to verify that2 q q 3+q qq 2 1− q(1− )/2=o(q !)(q →∞).(12)From the Stirling formula (7),qq > 1 (1− )1− q ,we obtain that (12)is satisfied uniformly over 0if we havesup ∈(0, 0]g( )<−1,(13)whereg( )=log 2 −log (1− )1− +log ⎛⎝ 2 1−1− 2⎞⎠.It is easy to see that the functionslog 2 ,−log (1− )1− ,log ⎛⎝ 21− 1− 2⎞⎠are increasing on (0,1/3].Hence,g is also increasing,and (13)follows from the inequality g( 0)<−1.This completes the proof of the corollary.In a future paper we are planning to adopt the method of Theorem 2to prove an asymptotic formula for N q (k 1,...,k d )with arbitrary k 1,...,k d where d is fixed or grows slowly as q →∞.It would be of interest to enumerate permutation polynomials with degree approx-imately √q .Unfortunately our approach cannot reach this range since we know that for q odd there are no permutation polynomials of degree (q −1)/2(see[3,Corollary 7.5]).AcknowledgmentsThe authors are grateful to the referee for a careful reading of the paper.Due to his (or her)remarks,a series of shortcomings have been corrected.Also,we would like toS.Konyagin,F.Pappalardi/Finite Fields and Their Applications12(2006)26–3737 thank Igor Shparlinski and Mike Zieve for useful comments on thefirst draft of this paper.The project was performed during the visit of thefirst author to the UniversitàRoma Tre in April2003supported in part by G.N.S.A.G.A.from Istituto Nazionale di Alta Matematica.References[1]P.Das,The number of permutation polynomials of a given degree over afinitefield,Finite FieldsAppl.8(2002)478–490.[2]S.Konyagin,F.Pappalardi,Enumerating permutation polynomials overfinitefields by degree,FiniteFields Appl.8(2002)548–553.[3]R.Lidl,H.Niederreiter,Finite Fields,second ed.,Cambridge University Press,Cambridge,1997.。