摘要
现代化的建设需要信息技术的支持，专家系统是一种智能化的信息技术，它的应用改变了过去社会各领域生产基层领导者决策的盲目性和主观性，缓解了我国各领域技术推广人员不足的矛盾，促进了社会的持续发展。但传统专家系统只能处理显性的表面的知识，存在推理能力弱，智能水平低等缺点，所以本文引入了神经网络技术来克服传统专家系统的不足，来试图解决专家系统中存在的关系复杂、边界模糊等难于用规则或数学模型严格描述的问题。本文采用神经网络进行大部分的知识获取及推理功能，将网络输出结果转换成专家系统推理机能接受的形式，由专家系统的推理机得到问题的最后结果。最后，根据论文中的理论建造了棉铃虫害预测的专家系统，能够准确预测棉铃虫的发病程度，并能给用户提出防治建议及措施。有力地说明了本论文中所建造的专家系统在一定程度上解决了传统专家系统在知识获取上的“瓶颈”问题，实现了神经网络的并行推理，神经网络在专家系统中的应用具有较好的发展前景。
关键词神经网络专家系统推理机面向对象知识获取
Abstract
Modern construction needs the support of IT, expert system is the IT of a kind of intelligence, its application has changed past social each field production subjectivity and the blindness of grass-roots leader decision-making, have alleviated the contradiction that each field technical popularization of our country has insufficient people, the continued development that has promoted society. But traditional expert system can only handle the surface of dominance knowledge, existence has weak inference ability, intelligent level is low, so this paper has led into artificial neural network technology to surmount the deficiency of traditional expert system, attempt the relation that solution has in expert system complex, boundary is fuzzy etc. are hard to describe strictly with regular or mathematics model. This paper carries out the most of knowledge with neural network to get and infer function , changes network output as a result into expert system, inference function the form of accepting , the inference machine from expert system gets the final result of problem. Finally, have built the expert system of the cotton bell forecast of insect pest according to the theory in this thesis, can accurate forecast cotton bell insect become sick degree, and can make prevention suggestion and measure to user. Have proved on certain degree the expert system built using this tool have solved traditional expert system in knowledge the problem of " bottleneck " that gotten , the parallel inference that has realized neural network, Neural network in expert system application has the better prospect for development.
Key words Neural network Expert system Reasoning engine
Object-orientation Knowledge acquisition
目录
摘要........................................................... I Abstract........................................................ II 第1章绪论 (1)
1.1 论文研究的背景 (1)
1.1.1 国内外研究现状 (1)
1.1.2 专家系统在开发使用中存在的缺点 (2)
1.1.3 神经网络的局限性 (3)
1.2 论文研究的主要内容 (3)
1.3 论文研究的目标及意义 (4)
1.4 论文的组织结构和安排 (4)
第2章神经网络和专家系统的基本理论 (5)
2.1 神经网络的基本理论 (5)
2.1.1 神经网络的概述及工作原理 (5)
2.1.2 神经网络的基本特征及优点 (6)
2.1.3 BP神经网络模型 (8)
2.1.4 BP网络结构设置 (10)
2.2 专家系统的基本理论 (12)
2.2.1 专家系统的功能 (12)
2.2.2 专家系统的基本结构及组成 (13)
第3章基于神经网络专家系统的研究 (16)
3.1 神经网络专家系统整体设计 (16)
3.1.1 神经网络专家系统总体结构 (16)
3.1.2 神经网络专家系统的组成及功能 (16)
3.2 知识表示 (17)
3.2.1 传统知识表示方法 (18)
3.2.2 面向对象知识表示方法 (19)
