Application of fuzzy logic in content-based image retrieval

Traffic lightsSignal control is a necessary measure to maintain the quality and safety of traffic circulation. Further development of present signal control has great potential to reduce travel times, vehicle and accident costs, and vehicle emissions. The development of detection and computer technology has changed traffic signal control from fixed-time open-loop regulation to adaptive feedback control. Present adaptive control methods, like the British MOVA, Swedish SOS (isolated signals) and British SCOOT (area-wide control), use mathematical optimization and simulation techniques to adjust the signal timing to the observed fluctuations of traffic flow in real time. The optimization is done by changing the green time and cycle lengths of the signals. In area-wide control the offsets between intersections are also changed. Several methods have been developed for determining the optimal cycle length and the minimum delay at an intersection but, based on uncertainty and rigid nature of traffic signal control, the global optimum is not possible to find out.As a result of growing public awareness of the environmental impact of road traffic many authorities are now pursuing policies to:− manage demand and congestion;− influence mode and route choice;− improve priority for buses, trams and other public service vehicles;−provide better and safer facilities for pedestrians, cyclists and other vulnerable road users;− reduce vehicle emissions, noise and visual intrusion; and− improve safety for all road user groups.In adaptive traffic signal control the increase in flexibility increases the number of overlapping green phases in the cycle, thus making the mathematical optimization very complicated and difficult. For that reason, the adaptive signal control in most cases is not based on precise optimization but on the green extension principle. In practice, uniformity is the principle followed in signal control for traffic safety reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic signal control in practice are based on tailor-made solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and fine-tuning in the field is needed. Fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human can control the process operator. Thus, traffic signal control in particular is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potentials of fuzzy control is a simulation of traffic signal control in an inter-section of twoone-way streets. Even in this very simple case the fuzzy control was at least as good as the traditional adaptive control. In general, fuzzy control is found to be superior in complex problems with multiobjective decisions. In traffic signal control several traffic flows compete from the same time and space, and different priorities are often set to different traffic flows or vehicle groups. In addition, the optimization includes several simultaneous criteria, like the average and maximum vehicle and pedestrian delays, maximum queue lengths and percentage of stopped vehicles. So, it is very likely that fuzzy control is very competitive in complicated real intersections where the use of traditional optimization methods is problematic.Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The main benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decision-making. Moreover, fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategies based on priori communication. The point in utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itself. Indeed, fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human operator can control process. In general, fuzzy control is found to be superior in complex problems with multi-objective decisions.At present, there is a multitude of inference systems based on fuzzy technique. Most of them, however, suffer ill-defined foundations; even if they are mostly performing better that classical mathematical method, they still contain black boxes, e.g. de fuzzification, which are very difficult to justify mathematically or logically. For example, fuzzy IF - THEN rules, which are in the core of fuzzy inference systems, are often reported to be generalizations of classical Modus Ponens rule of inference, but literally this not the case; the relation between these rules and any known many-valued logic is complicated and artificial. Moreover, the performance of an expert system should be equivalent to that of human expert: it should give the same results that the expert gives, but warn when the control situation is so vague that an expert is not sure about the right action. The existing fuzzy expert systems very seldom fulfil this latter condition.Many researches observe, however, that fuzzy inference is based on similarity. Kosko, for example, writes 'Fuzzy membership...represents similarities of objects to imprecisely defined properties'. Taking this remark seriously, we study systematically many-valued equivalence, i.e. fuzzy similarity. It turns out that, starting from the Lukasiewicz well-defined many-valued logic, we are able to construct a method performing fuzzy reasoning such that the inference relies only on experts knowledge and on well-defined logical concepts. Therefore we do not need any artificial defuzzification method (like Center of Gravity) to determine the final output of the inference. Our basic observation is that any fuzzy set generates a fuzzy similarity, and that thesesimilarities can be combined to a fuzzy relation which turns out to a fuzzy similarity, too. We call this induced fuzzy relation total fuzzy similarity. Fuzzy IF - THEN inference systems are, in fact, problems of choice: compare each IF-part of the rule base with an actual input value, find the most similar case and fire the corresponding THEN-part; if it is not unique, use a criteria given by an expert to proceed. Based on the Lukasiewicz welldefined many valued logic, we show how this method can be carried out formally.Hypothesis and Principles of Fuzzy Traffic Signal Control Traffic signal control is used to maximize the efficiency of the existing traffic systems [6]. However, the efficiency of traffic system can even be fuzzy. By providing temporal separation of rights of way to approaching flows, traffic signals exert a profound influence on the efficiency of traffic flow. They can operate to the advantage or disadvantage of the vehicles or pedestrians; depend on how the rights of ways are allocated. Consequently, the proper application, design, installation, operation, and maintenance of traffic signals is critical to the orderly safe and efficient movement of traffic at intersections.In traffic signal control, we can find some kind of uncertainties in many levels. The inputs of traffic signal control are inaccurate, and that means that we cannot handle the traffic of approaches exactly. The control possibilities are complicated, and handling these possibilities are an extremely complex task. Maximizing safety, minimizing environmental aspects and minimizing delays are some of the objectives of control, but it is difficult to handle them together in the traditional traffic signal control. The causeconsequence- relationship is also not possible to explain in traffic signal control. These are typical features of fuzzy control.Fuzzy logic based controllers are designed to capture the key factors for controlling a process without requiring many detailed mathematical formulas. Due to this fact, they have many advantages in real time applications. The controllers have a simple computational structure, since they do not require many numerical calculations. The IFTHEN logic of their inference rules does not require much computational time. Also, the controllers can operate on a large range of inputs, since different sets of control rules can be applied to them. If the system related knowledge is represented by simple fuzzy IFTHEN- rules, a fuzzy-based controller can control the system with efficiency and ease. The main goal of traffic signal control is to ensure safety at signalized intersections by keeping conflict traffic flows apart. The optimal performance of the signalized intersections is the combination of time value, environmental effects and traffic safety. Our goal is the optimal system, but we need to decide what attributes and weights will be used to judge optimality.The entire knowledge of the system designer about the process, traffic signal control in this case, to be controlled is stored as rules in the knowledge base. Thus the rules have a basicinfluence on the closed-loop behaviour of the system and should therefore be acquired thoroughly. The development of rules is time consuming, and designers often have to translate process knowledge into appropriate rules. Sugeno and Nishida mentioned four ways to derive fuzzy control rules:1. operators experience2. control engineer's knowledge3. fuzzy modelling of the operator's control actions4. fuzzy modelling of the process5. crisp modeling of the process6. heuristic design rules7. on-line adaptation of the rules.Usually a combination of some of these methods is necessary to obtain good results. As in conventional control, increased experience in the design of fuzzy controllers leads to decreasing development times.The main goals of FUSICO-research project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules for traffic signal control using linguistic variables, validation of fuzzy control principles and calibration of membership functions, and development of a fuzzy adaptive signal controller. The vehicle-actuated control strategies, like SOS, MOVA and LHOVRA, are the control algorithms of the first generation. The fuzzy control algorithm can be one of the algorithms of the second generation, the generation of artificial intelligence (AI). The fuzzy control is capable of handling multi-objective, multi-dimensional and complicated traffic situations, like traffic signalling. The typical advantages of fuzzy control are simple process, effective control and better quality.FUSICO-project modelled the experience of policeman. The rule base development was made during the fall 1996. Mr. Kari J. Sane, experienced traffic signal planner, was working at the Helsinki University of Technology at this time. Everyday discussions and working groups helped us to model his experience to our rules.In particular pathological traffic jams or situations where there are very few vehicles in circulation; there first-in-first-out is the only reasonable control strategy. The Algorithm is looking for the most similar IF-part to the actual input value, and the corresponding THEN-part is then fired. Three realistic traffic signal control systems were constructed by means of the Algorithm and a simulation model tested their performance. Similar simulations were made to a non-fuzzy and classical Mamdani style fuzzy inference systems, too. The results with respect to average vehicle and pedestrian delay or average vehicle delay were in most cases better on fuzzy similarity based control than on the other control systems. Comparisons between fuzzysimilarity based control and Mamdani style fuzzy control also strength an assumption that, in approximate reasoning, a fundamental concept is many-valued similarity between objects rather than a generalization of classical Modus Ponens rule of inference.The results of this project have indicated that fuzzy signal control is the potential control method for isolated intersections. The comparison results of Pappis-Mamdani control, fuzzy isolated pedestrian crossing and fuzzy two-phase control are good. The results of isolated pedestrian crossing indicate that the fuzzy control provides the effective compromise between the two opposing objectives, minimum pedestrian delay and minimum vehicle delay. The results of two-phase control and Pappis-Mamdani control indicate that the application area of fuzzy control is very wide. The maximum delay improvement was more than 20 %, which means that the efficiency of fuzzy control can be better than the efficiency of traditional vehicle-actuated control.According to these results, we can say that the fuzzy signal control can be multiobjective and more efficient than conventional adaptive signal control nowadays. The biggest benefits can, probably, be achieved in more complicated intersections and environments. The FUSICO-project continues. The aim is to move step by step to more complicated traffic signals and to continue the theoretical work of fuzzy control. The first example will be the public transport priorities.REFERENCES1. M.G.H. Bell, Future Directions in Traffic Signal Control, Transportation Research 26 (992) 303-313.2. R. Cignoli, M.L. D'Ottaviano, D. Mundici, Algebraic Foundations of many valued Reasoning, to appear.3. P. H'ajek, Metamathematics of fuzzy logic, Kluwer Acad. Publishers, Dordrecht, 1998.4. U. H"ohle, On the Fundamentals of Fuzzy Set Theory. J. of Math. Anal. and Appl. 201 (1996) 786-826.5. J. Niittym"aki, Isolated Traffic Signals - V ehicle Dynamics and Fuzzy Control, Thesis, Helsinki University of Technology, 1997.6. J. Niittym"aki, S. Kikuchi, Application of Fuzzy Logic of a Pedestrian Crossing Signal, Transportation Research Record No 1651. Intelligent Transportation Systems, Automated Highway Systems, Travel Information, and Artificial Intelligence. Washington D.C. 1998.7. B. Kosko, The probability monopoly, IEEE Transactions of fuzzy systems, 2 (1994) 32-33.8. C. Pappis, E. Mamdani, A fuzzy logic controller to a traffic junction,IEEE transaction on systems, man and cybernetics V ol SMC-7 No 10, 1977, 707-717.9. J. Pavelka, On fuzzy logic, I,II,III Zeitsch. f. Math. Logik. 25 (1979), 45-52, 119-134, 447-464.10. D. Teodorovic, Fuzzy logic systems for transportation engineering: the state of the art. Transportation Research Part A 33 (1999) 337-364.11. M. Sugeno, M. Nishida, Fuzzy control to model car. Fuzzy Sets and Systems 16 (1985) 103-113.12. E. Turunen, Mathematics behind Fuzzy Logic, Advances in Soft Computing, Physica- V erlag, Heidelberg, 1999.13. L. Zadeh, Fuzzy Sets, Information and Control 8 (1965) 338-353. 16. H.-J. Zimmermann, Fuzzy Set Theory, Kluwer, 1996.14. Optimising fuzzy logic traffic signal control systems, Stuart Clement , .au/tsc/index.html, Transport Systems Centre, University of South Australia.15. Fuzzy Traffic Signal Control - Principles and Applications Jarkko Niittymäki, Dissertation for the degree of Doctor of Science in Technology, Department of Civil and Environmental Engineering ,Helsinki University of Technology, Espoo, Finland.16. Traffic Signal Control on Total Fuzzy Similarity based Reasoning, Jarkko Niittym"aki Helsinki University of Technology, P.O. Box 2100, FIN-02015 HUT, Finland17. Chiu, S and Chand, S (1992) Adaptive traffic signal control using fuzzy logic, pp 98-107 of Proceedings of the Intelligent V ehicles Symposium Detroit, Michigan, USA.Clement, SJ Bell, MGH Cassir, C and Grosso, S (1997a) Experiences with the Path Flow Estimator on a Leicester City street network,交通灯信号控制是一种必要的措施以确保的质量和安全,交通循环。

电力故障诊断方法研究的一些参考文献Research on the method of power failure diagnosis has been a crucial area of focus in the field of electrical engineering. One important reference in this area is the paper "Power Proxy: Anomaly Detection in Power Usage Data" by Zhang et al. This paper proposes a novel approach using deep learning techniques to detect anomalies in power usage data, which can aid in the diagnosis of power failures. The authors demonstrate the effectiveness of their method through experiments on real-world power usage datasets.电力故障诊断方法的研究一直是电气工程领域的一个重要研究方向。

张等人的论文《Power Proxy: Anomaly Detection in Power Usage Data》是这方面的一个重要参考文献。

这篇论文提出了一种新颖的方法，利用深度学习技术来检测电力使用数据中的异常，有助于诊断电力故障。

作者通过对真实电力使用数据集的实验验证了他们方法的有效性。

In addition to Zhang et al.'s work, another valuable reference is the paper "A Survey on Fault Diagnosis Techniques Through EE Stream Processing" by Wang et al. This survey paper provides a comprehensive overview of fault diagnosis techniques in the contextof electrical engineering stream processing. The authors discuss various methods such as Bayesian networks, neural networks, and decision trees, highlighting their applications in diagnosing power failures. This paper serves as a useful guide for researchers interested in exploring different fault diagnosis techniques.除了张等人的工作，王等人的论文《A Survey on Fault Diagnosis Techniques Through EE Stream Processing》也是一个很有价值的参考文献。

Identification of sport talents using a web-oriented expert systemwith a fuzzy moduleNumerous sport clubs, parents and sportsmen are permanently seeking the answer to the question: how to recognize a talented child and which sport is the most appropriate for him or her? The correct answer is not trivial at all because it demands adequate input information about the observed person, as well as the knowledge of what this information should include. In other words, expert knowledge is needed in order to predict the sport with the highest expectation rate for the observed individual, based on available data.This paper presents a fuzzy expert system for scouting and evaluation of young sport talents. Based on the knowledge of several human sport experts, various motoric skills tests, morphologic characteristics measurements and functional tests are quantized according to their importance for a chosen set of sports. Obtained values are entered into the knowledge database along with the grades of the measured results for each test. Fuzzy logic is implemented in order to make the system more flexible and robust. The whole system is web-oriented, i.e. developed application is available to the internet users with a proper login and password. The developed expert system gives acceptability prediction and proposal of the most suitable sports for the person being tested. The output results of the system were evaluated by 4 experts, using real data collected during several years. Comparison is done between the sport pro posed by our expert system and the actual outcome of the person’s sports career. Also, the comparison of the expert system output and the human expert suggestions were done. All tests showed high reliability and accuracy of the developed system. Strengths, possibilities and future plans of the Sport Talent expert system are also discussed.Similar methodology and knowledge can be implemented and used in order to predict future results of adult sportsmen, but a distinction should be made because areliable prediction for children is much more difficult. Changes during puberty can significantly influence the prospects of a future sportsman. However, extensive research that has been done in order to test, analyze and compare athletes of various sports brings precious information and knowledge that can be used for the sport talents identification, also.Comparison of children aged 8–16 can be done on the basis of normative test values. As one would expect, importance contribution of each test is not the same. Also, importance of each test varies according to the sport chosen. Implicitly, this statement is confirmed by Norton and Olds in their study of morphological evolution of athletes of various sports during the last century. Additionally, the study conducted by Norton and Olds brings important data regarding morphological trends that can be used for updating some normative values presented by Findak et al.. Based on the set of tests that are already present in elementary and secondary schools, previous research by the authors suggested that the problem’s solution should be based on expert and scientific knowledge of relevant motoric skills tests, morphologic characteristics measurements and functional tests . During our search for the right or satisfying solution of sports talent recognition, we should overcome two main problems. The first one is a very difficult task of finding an expert in this field, since the domain of specific knowledge is separated into various sports and, generally, experts have in-depth knowledge of the relevant factors for a specific sport and more superficial for other sp orts. The second problem is that the knowledge and the results obtained by the system’s output should be widely available, independent of the time of day and the conditions . All these facts lead to the decision of developing a computer based expert system . The attempt to bring expert knowledge closer to the users in the field requires use of new technology and possibilities that it brings. As a natural solution, the development of such a system should include accessibility through Internet.Expert systems methodologies may be cla ssified into eleven categories: rule-based systems, knowledge-based systems, neural networks, fuzzy expert systems, object-oriented methodology, case-based reasoning (CBR), system architecturedevelopment, intelligent agent (IA) systems, modeling, ontology, and database methodology together with their applications for different research and problem domains . Knowledge acquisition from the experts can be done using several approaches with different levels of automatization and determination procedures of the factors weights . Generally, knowledge acquisition techniques that are most frequently used today require an enormous amount of time and effort on the part of both the knowledge engineer and the domain expert. They also require the knowledge engineer to have an unusually wide variety of interviewing and knowledge representation skills in order to be successful . As a result, inclusion of the experts with the knowledge from both worlds, in the development of the expert system is a pre-request that should be satisfied if possible.The World Wide Web is emerging as an increasingly important platform that can reduce technological barriers and make it easier for users in different geographical locations to access the decision support models and tools . Existing stand-alone applications can be converted to the java-based web applications , but there are also other web-based ITS architectures that can be used. Internet based expert systems can have different architectures, such as centralized, replicated or distributed . This categorization is done according to the place where the code is executed . Another, similar categorization of the existing methodologies is into two categories, the server side and the client-side, depending on the location of the inference engine of a Web-enabled, rule-based system. The server-side category can be further divided into three more detailed categories, the CGI program, the server-side script, and the Web server embedded module, depending on the types of inference engine implemented. The client-side category may be classified into two sub-categories, the external viewer and the Java applet.Applications of the Web-enabled expert systems based upon client–server architecture for planning and decision making using a multi-agent approach are becoming more and more popular. Although most of the Web-enabled, rule-based systems have been developed using CGI technology, less burden to Web servers is present when the ASP as the server-side script approach is used.Expert system applications development is a problem-oriented domain. V ery generally speaking, our interest can be described as the evaluation of a particular subject according to some demands or rules . V agueness of expert knowledge, grades and some other data needed for the solution of our problem resulted in the necessity of fuzzy logic implementation and the approach that can, in some aspects of fuzzy logic implementation, be compared to the solution proposed byWeon and Kim or the system developed by Bai and Chen for the evaluation of students’ learning achievement.The use of the expert systems for the assessment of sports talent in children have been reported in the past . Some results obtained by this research were used for the development of a more specific expert system for the basketball performance prediction and assessment . Neither of these systems has used web technologies and, as a consequence, has some limitations that can now be overridden. An expert system should be adaptive to constant changes of new standard values and measures as well as open to insertion of new knowledge. Bases of the approach proposed by the authors are described and presented in but further development and evaluation of the system showed that there are many questions left unanswered. Also, lots of possible improvements regarding methodology, technology and a scope of a possible application can be done. One of the most important improvements concerning reliability of acquired expe rt knowledge and the desired system flexibility is the introduction of fuzzy logic. More on fuzzy logic and fuzzy sets will be explained in the following section. System adjustments are done after an evaluation of the expert system that was made possible af ter extensive field research that resulted in collecting a large set of reliable test data.Our software based solution has the following characteristics: ability of forming a referent measurement database with the records of all potential and active sportsmen, diagnostics of their anthropological characteristics, sports talent recognition, advising and guiding amateurs into the sports activities suitable for their potential. Also, a comparison of the test results for the same person and for overall achievement monitoring through a longer time period is possible.译文：体育人才的识别使用以网络为中心的专家系统众多的体育俱乐部，家长和运动员都将被永久寻求问题的答案：如何识别一个有才华的孩子，该运动是他或她最合适？所有正确的答案是不平凡的，因为它要求对所观察到的人，这是什么信息应包括知识以及足够的输入信息。

CONTROLEDGE™The ControlEdge HC900 is fully validated to perform its safety tasks, and is certified by TUV for use in a SIL-2 environment. The system isideal for a process/safety environment. Its non-interfering software meansthat the ControlEdge HC900 system iscapable of hosting process control andsafety applications, providing control,monitoring, password protectionfor configuration, alarm processingand data acquisition for processapplications thus adding to reliabledata and information being stored andprotected.TUV Compliance TYPICAL INDUSTRIES• Chemicals (including specialty andfine chemicals, plastics and rubber)• Life sciences and Cosmetics• Power (excluding nuclear)• Cement and Glass• Pulp and Paper• Mining and Metals• Water and Wastewater• Food and Beverage• Heat Treatment• Buildings/ Infrastructure-Metro Rail, HVAC etc.CUSTOMER BENEFITSProven & ReliableMaximizes uptime• Proven track record• Redundancy• G3 conformal coating forharsh environment• RoHS compliant• Actionable informationHigh PerformanceIncreases profitability• Tighter control• Reduced scrap• Higher throughputEasy to Use & EngineerLowers operational costs• Easy engineering• Faster startup• Simple intuitive tools• No additional maintenance feesEnhanced Safetywith SIL2 certification• Common hardware andsoftware for process and safety• Improved plant safetyTYPICAL APPLICATIONSSafety• Burner Management Systems (e.g.furnaces, boilers, ovens, pre-heaters,reactors, calciners, dryers, thermaloxidizers, kilns, melters, incinerators,process heaters, vaporizers)• Combustion Control• Pipeline Monitoring• Spill Prevention• Metro/ Road Transportation-Tunnel Safety, Ventilation• Wastewater Treatment• Terminal Automation• Emergency Shutdown• Fire & Gas Monitoring• Pressure and Flow ControlCritical Control• Electronics & SemiConductor• Cement and Glass• TextilesCertifications• TUV SIL2• CSA-Canada and USA(HazLoc) / FM CL1 / Div2• ATEX• ABS• UL• CE• RoHSsuch as burner management systems (BMS), emergency shutdown systems (ESD), fire & gas monitoring, pipeline monitoring, spill prevention, tunnel ventilation, etc.MUL TIPLE SYSTEMS,MUL TIPLE I/O RACKSControlEdge HC900 Hot Stand-by ArchitectureCPU CapacityControlEdge HC900 Designer Software• Configuration: ControlEdge HC900 Controller – offline with run-mode editing • Operating environment: Windows 7 Pro (32 or 64-Bit), Win 8 and Win 10• PC: Pentium, 1.5 GHz with 1 GB RAM minimum, SVGA or greater screen resolution • English and Mandarin languages supported (switchable after installation)• Cable: RS 485 – three-wire, Ethernet 10/100 base T • Modem support: Monitor, upload, download configuration• New input voting (1oo2 and 2oo3) and outputvalidation function blocks (with feedback verification)• New non-interfering process and safety worksheets in • Change management and Version Control built into the software same configuration• Function Blocks: C70, C75 CPU–15000, C50 CPU–2000, C30 CPU–400• Controller C75 CPU supports redundancy (in common and separate rack). Redundant CPU racks can be placed at least 1km apart • Analog Inputs: Up to 1152 universal analog inputs, 2304 high level, A/D Resolution is ±15 Bits • Accuracy: 0.1% of span(field calibration to ±0.05% of span)• Analog Outputs: Up to 480 withinternal power, 2304 with external power 0 to 20 mA maximum, 0.1% accuracy • Universal SIL IO Module (UIO)- 16 channel, with 1ms SOE, HART, Line Monitoring, Voting & Validation and IO redundancy • HART-IP support for improved device diagnostics and easy maintenance • Digital Inputs / Outputs: Up to 4608, contact DI, 24 Vdc DI / DO, 120VacDI /DO, 240 Vac DI/DO • Total I/O: Up to 4608• I/O Racks per System: One controller and up to 11 remote I/O racks• Control Loops: PID, on/off, cascade, ratio, %C, three-position step• Control Output Types: Current, time -proportioning, position-proportioning, three-position steps • New Input Voting (1oo2 and 2oo3) and outputvalidation function blocks (with feedback verification)• Setpoint Programmers: 50 segments each, 16 event outputs, multiple stored profiles • Setpoint Scheduler: 50 segments, 8 ramp / soak outputs, eight auxiliary outputs, 16 events, multiple schedules• Communications: Ethernet 10 / 100/ base T,Modbus / TCP protocol, up to 10 Ethernet hosts on C50, C70, C75 up to 32 peer-to-peer controllers, Serial Modbus RTU, RS485, slave or master operation (up to 32 slaves), HART-IP for Analog signals • Operating Temp: Rated 32° to 140°F (0° to 60°C)• Humidity: Rated 10% RH to 90% RH, non-condensingControllerMODULAR AND SCALABLE• Available in three rack sizes and three CPU performance levels• Handles a wide range of automation requirements• Analog and digital modules support up to 4608 I/O points • Scalable and expandable• Easy to own, engineer,operate and maintain•Upto 12 racks and 4608 IO’sFUNCTION BLOCKS• Simplify execution of complex control strategies• Over 125 different types of softwarefunction blocks available• Each function block represents a uniquealgorithm for a specific control function• Available CPU options support up to 400,2,000 or 15,000 function blocks• Simply drag and drop, and soft-wire• 1oo2 and 2oo3 Voting Function Blocks.DO-V and AO-V (Digital Output and AnalogOutput Validation Function blocks).SEPARATE PROCESS SAFETY WORKSHEETS• Same type of controller can be used for process andsafety applications thus reducing total cost of ownership• Provisions are provided within programming environmentto program using safety/process worksheetsUNIVERSAL ANALOG INPUTS• Accept both direct and indirect inputs from sensors• Minimize the number of input cardsand spare parts required• Inputs may be mixed on a module and mayinclude multiple thermocouple types, RTDs,ohms, voltage or millivoltage types.SIL UNIVERSAL IO MODULE• 16 channel user configurable to DI, DO, AI or AO• High resolution SOE with 1 ms time stamp• Line Monitoring (Open Wire, Short Circuit Detection)• HART support for Analog signals• In-built Voting & ValidationCONTROL LOOPS• Provide tighter, more accurateprocess control• Include applications ranging from single loop control to interactive cascade, ratio, duplex, feed-forward, three-position-step, or custom controlled strategies • Increase throughput, reduce scrap, and minimize energy costs•Quantity of loops per controller is not limitedFUZZY OVERSHOOT SUPPRESSION• Fuzzy Overshoot Suppression minimizesthe Process Variable (PV) overshootfollowing a Setpoint (SP) change or aprocess disturbance. This is especiallyuseful in processes that experienceload changes or where even a smallovershoot beyond the setpoint mayresult in damage or product loss.• The Fuzzy Logic in the controller observesthe speed and direction of the PV signalas it approaches the setpoint andtemporarily modifies the internal controllerresponse action as necessary to avoidan overshoot. There is no change to thePID algorithm, and the Fuzzy Logic doesnot alter the PID tuning parameters.CARBON POTENTIAL• The carbon potential of the furnaceatmosphere can be controlled bymonitoring the furnace temperature andthe probe output because oxygen potentialdirectly relates to the carbon potential.A combined carbon probe, temperatureprobe and PID algorithm determinecarbon potential of furnace atmospheresbased on a zirconium probe input.• Activates anti-sooting feature thatlimits the working setpoint of thecarbon control loop to a value thatprevents sooting in the furnace.FREE-FORMAT LOGIC• Optimizes design by combining multiple logic functions into one • Simplifies operation and troubleshootingSEQUENCERS• Control the output states ofmultiple digital parameters• Control the sequence of process operation based on time or process events • Each sequencer supports up to 16 digital outputs and may have up to 50 process states • Multiple sequences can be selected on demand from the operator interface oras part of a recipeRECIPES• Stored in the controller memory • Ensure error-free product/process changeovers• Write values into analog and digital variables • Load via Control Station • Load via RCP block • Can be used to:– Write a value to any variable – Load setpoints– Select setpoint programs – Set alarm limits– Activate control valvesSETPOINT PROGRAMMER• Automatically manipulates a setpoint value for use by PID loops • Creates a time / value profile for process batch control• Multiple setpoint programmers, with profiles of up to 50 segments each, may be configured and stored • Any programmer may run any profile separately or simultaneously• Each also has an auxiliary soak output and up to 16 event outputs for integration with sequence control functionsDEW POINT CONTROL• Dew point analysis measures the amount of water vapor present which in turn helps determine the carbon potential of a furnace atmosphere • This application uses the dew point function block to calculate dew point based on using a carbon probe where the input is an O2 sensor • A typical example is control of an endothermic atmosphere generator when the user requires dew point for PVSETPOINT SCHEDULER• Provides up to eight ramp/soak setpoints that operate on a common time base • Supports up to 16event digital outputs 50 segments per schedule; the number of storedschedules is configurable • Auxiliary Scheduler provides an additional 8 Soak Setpoints • Multiple independent setpoint schedulers areavailable in a configurationREDUNDANCY• Maximize process availability by providing backupcontrollers, power supplies and communications for seamless failover under fault conditions • Redundant Switch Module (RSM) is located in the rack between two CPUs and visuallyindicates which CPU is the lead and which is the reserve• Key switch on the RSM allows the user to change the operating mode of the lead and reserve CPUs • Ethernet network ports are continuously active on the leadcontroller, each on a different subnet • Transfer of communicationsfrom one port to another port on the same CPU is handled by the host application • A secondary power supply can also be added to eachControlEdge 900 Platform I/O rack for standby redundancy • Supports redundant I/Oconfiguration in Universal ModuleAMS COMPLIANCE• The ControlEdge HC900 meets AMS 2750E, the key requirement for controlling, monitoring and recording instruments, which is acalibrated accuracy (± 2° F/1.1°C)FLEXIBLECONNECTIVITY SUITS YOUR PROCESSENVIRONMENTOPEN ETHERNET CONNECTIVITY• Enables ControlEdge HC900 controllers to communicate with their host interfaces and each other • Open Modbus / TCP protocol allows interfacing to most popular HMI, data acquisition and OPC software • Up to 10 device connections are supported on the host Ethernet port • ControlEdge HC900 network of controllers and operator interfaces are partitioned into segments on the network to maximize communication performanceSERIAL ETHERNET CONNECTIVITY• Allows two RS485 ports to be configured as Modbusslaves, while one of the ports is selected as a Modbus master • Wide variety of devices (touch panel operator interfaces, I/O devices, etc.) can be connected to the controller • Provides greater flexibility in system designCONNECTIVITY AND COMMUNICATIONS• Adapts to existing process-line infrastructure • Satisfies specific control requirements• Accommodates specialtyapplicationsPEER-TO-PEER COMMUNICATIONS• The improved ControlEdge HC900 controllers provide peer-to-peer interface between a maximum of 32 units for process/safetyequipment applications that require sharing data between controllers.• Up to 2,240 parameters per controller may be exchanged • Standard Ethernet communication port supports concurrent peer-to-peer communications andconnectivity to supervisory systems• Peer-peer between safety systems is done using the new Safety-peer protocol that can exchange safety critical data between peersINTEGRATION WITH EXPERION• ControlEdge HC900 controllers can be integrated with the Honeywell Experion DCS system for supervisory control and data acquisition• Can be integrated with Experion PKS, LX and HS systems Configured using Quick Builder application • Uses Universal Modbus Driver for communication • Redundant controllers can also be integrated with ExperionCONTROLEDGE HC900 OPC SERVER FROM MATRIKONOPC• Provides secure and reliable real-time data access between the ControlEdge HC900 Controller and any OPC-enabled applications such as Historians, HMIs, SCADA etc.• Enables 3rd party connectivity for successful phased migration and integration • Enables easy and cost-efficient management of openly connected systemsBUILDING-BLOCK CONFIGURATION SIMPLIFIES CONTROLIMPLEMENTATIONCONTROLEDGE HC900 DESIGNER SOFTWARE• Enables system configuration with a Windows 7 (32 or 64-bit), Win 8 and Win 10 based PC • English and Mandarin languages supported (switchable after installation) • Uses drag-and-drop placement techniques for graphic icons and soft-wiring connections between function blocks • Automatically calculates memory usage and processor scan time as function blocks are configured • User-friendly graphic development allows partitioning of the control strategy into multiple worksheets• Ease of record-keeping, faster access to functional areas during programming • Better support for user-specified process function identificationsCONFIGURATION DEBUG TOOLS• Simplify troubleshooting Include online monitoring of multiplefunction blocks on a single display, on/off identification of digital signal flow connections, and output forcing capability for most block outputs • Selectable user-defined Watch Windows and Signal Trace-back provide a clear view of the configuration operation and quick identification of potential errorsPRINTABLE PRESENTATION FORMATS• Simplify configuration documentation• Include a summary of controller I/O, the graphic configuration diagram, function block properties, recipe groups, setpoint profile groups, operator display and point selectionRUN-MODECONFIGURATION EDITING• Standard feature that cansignificantly reduce start-up time and avoid costly process shutdowns• History Backfill: If Experion HS loses communication with ControlEdge HC900 the History backfill functionality will backfill data to the HMI once communication is restored. This feature is very important for food and beverage, pharmaceutical and other data critical applications and is not often provided by other general purpose PLC’s• Paired with Experion HS,ControlEdge HC900 can meet FDA 21 CFR part 11 requirements for pharmaceuticals, food and beverage industry that need a proven and reliable solution capable of change management, automated electronic recordkeeping, and advanced controller security and protection methods • Seamless integration with FDM for HART signalsSYSTEMCONFIGURATION AND OPERATION ADAPT TO YOUR NEEDSThe 900 Control Station operator interfaceStandard displays provided inControlEdge HC900 Control Station• NEMA Type 4X operator interface screen withstands harsh operating environments • Easy-to-operate 10” and 15” touch screen display • Standard and custom graphic elements can be assembled into specific displays, for fast and easy start-up• Custom graphics tools let you select from 4,000+ pre-built objects for animation support, math, formulas, scripting• Function block widgets accelerate configuration development • Controller status displays verify system integrity, with no configuration required• Recipe selection makesproduct/process changeovers simple and accurate • Trending and data logging is provided via SD card storage • Multi-level log-on security feature prevents unauthorized access • Alarm/Event logging withe-mail notification of impending problems tracks process upsets and validates performance • Ethernet or serial connectivity enhance installation flexibility, includes Modbus and Modbus\TCP protocol support • Embedded web serverfeature allows access to your application from anywhere• Multiple interfaces on each controller enableprocess management from up to three locations• Multilingual: English, French, Italian, German, Spanish. Other languages may be added by expanding its lexicon library• The software also supportsaccessing the translation libraries of Microsoft® and / or Google® for any untranslatable text strings used in the product during configuration • Setpoint Programmer Pre-PlotDisplay: Pre-plot display is a Widget that gets bound to a Setpoint Programmer function block • Concurrent Batch Reports: Schedules multiple batch reports to run concurrentlyOPERATOR INTERFACE FEATURESStandard displays in station designer 4 - WidgetsGraphic symbols provided in Station Designer software tosimplify configuration. Hundreds of icons availableincluding pumps, valves, and tanks icons shown.CONTROL STATION OPERATOR INTERFACE• The 900 Control Station is available with either a 10.4 inch (254 mm) or 15 inch (381mm) display size • LCD Display: 10” (800 x 600), 15” (1024 x 768) pixels, color active matrix thin film transistor (TFT), 16M colors • Touch Screen: Resistive analog• Backlight: 50,000 hr typical lifetime at room temperature (field replaceable in non-hazardous locations)• Distance from Controller: Ethernet-328 ft (100 m), RS485 - 2000ft (600 m) RS232 - 50ft (15.24 m)• Power Supply 10 inch: 24 Vdc, 16 Watts maximum 15 inch 22 Watts maximum without options • Operating Temperature: 14 to 122 °F, (-10 to 50 °C)• Humidity: Rated 0 to 85%, non-condensing from 14 to 122 °F, (-10 to 50 °C)• Panel Rating: Type 4X / IP66• Memory: 512MB onboard non-volatile flash, optional SD card• Communication Ports: 10 inch (254 mm) 1 x Ethernet RJ45 10/100 base T, 15 inch (381mm) 2 x Ethernet 10/100 base T, 2 x RS-485, 2 x RS232 Serial • USB Ports: 2 x USB specification 2.0 host port, type A, 1 x USB specification 2.0 device port type B900 STATION DESIGNER SOFTWARE• Configuration: 900 control station CR interface – offline • Operating environment: Windows 7 (32, 64-bit), Windows 8, Windows 10• PC: Pentium class processor and RAM as required by the chosen operating system plus 50MB for software installation, 800 by 600 pixels minimum, 256 or more colors. RS-232 or USB port•Cable: USB Host, RS232 Serial, Ethernet 10/100 base TEASY ENGINEERING AND FLEXIBILITY THROUGH USER FRIENDL Y TOOLSCONFIGURATION COMPARISON• Change management • Save engineering hours in finding previous changesBULK EDIT• Reduces engineering hoursVERSION CONTROL• Easy tracking, de-bugging • Revert to earlier versions • Save dollars for a separate version control offeringPASTE SPECIAL• Saves 15-20% of engineering effortsCHANGEMANAGEMENT• Manage versions, track and compare configurations • Easier troubleshooting thereby reducing maintenance costsPASSWORD PROTECTION• Controllers are password protected and thus prevents any intrusion through the network • Any changes to thecontroller are monitored and validated with credentialsRE-USABLE CUSTOM LIBRARIES• Save engineeringtime, create logic once and avoid errorsControlEdge™ is a trademark and Experion® is a registered trademark of Honeywell International Inc.BR -20-42-ENG | 10/2020© 2020 Honeywell International Inc.For more informationTo learn more about Honeywell’s ControlEdge HC900, visit or contact your Honeywell account manager.Honeywell Process Solutions2101 CityWest Blvd, Houston TX 77042Honeywell House, Skimped Hill Lane Bracknell, Berkshire, England RG12 1EB UK Building #1, 555 Huanke Road, Zhangjiang Hi-Tech Industrial Park, Pudong New Area, Shanghai 。

智能科学与技术专业英语一、单词1. Artificial Intelligence (AI)- 英语释义：The theory and development ofputer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision - making, and translation between languages.- 用法：“Artificial Intelligence” is often abbreviated as “AI” and can be used as a subject or in phrases like “AI technology” or “the field of AI”.- 双语例句：- Artificial Intelligence has made great progress in recent years. (近年来，人工智能取得了巨大的进展。

)- Manypanies are investing heavily in artificial intelligence research. (许多公司正在大力投资人工智能研究。

)2. Algorithm- 英语释义：A set ofputational steps and rules for performing a specific task.- 用法：Can be used as a countable noun, e.g. “T his algorithm is very efficient.”- 双语例句：- The new algorithm can solve the problem much faster. (新算法可以更快地解决这个问题。

模糊控制考核论文姓名：郑鑫学号：1409814011 班级：149641 题目:模糊控制的理论与发展概述摘要模糊控制理论是以模糊数学为基础,用语言规则表示方法和先进的计算机技术,由模糊推理进行决策的一种高级控制策。

模糊控制作为以模糊集合论、模糊语言变量及模糊逻辑推理为基础的一种计算机数字控制，它已成为目前实现智能控制的一种重要而又有效的形式尤其是模糊控制和神经网络、遗传算法及混沌理论等新学科的融合，正在显示出其巨大的应用潜力。

实质上模糊控制是一种非线性控制，从属于智能控制的范畴。

模糊控制的一大特点是既具有系统化的理论，又有着大量实际应用背景。

本文简单介绍了模糊控制的概念及应用，详细介绍了模糊控制器的设计，其中包含模糊控制系统的原理、模糊控制器的分类及其设计元素。

关键词：模糊控制；模糊控制器；现状及展望Abstract Fuzzy control theory is based on fuzzy mathematics, using language rule representation and advanced computer technology, it is a high-level control strategy which can make decision by the fuzzy reasoning. Fuzzy control is a computer numerical contro which based fuzzy set theory, fuzzy linguistic variables and fuzzy logic, it has become the effective form of intelligent control especially in the form of fuzzy control and neural networks, genetic algorithms and chaos theory and other new integration of disciplines, which is showing its great potential. Fuzzy control is essentially a nonlinear control, and subordinates intelligent control areas. A major feature of fuzzy control is both a systematic theory and a large number of the application background.This article introduces simply the concept and application of fuzzy control and introduces detailly the design of the fuzzy controller. It contains the principles of fuzzy control system, the classification of fuzzy controller and its design elements.Key words: Fuzzy Control; Fuzzy Controller; Status and Prospects.引言传统的常规PID控制方式是根据被控制对象的数学模型建立，虽然它的控制精度可以很高，但对于多变量且具有强耦合性的时变系统表现出很大的误差。

About the T utorialThis tutorial provides introductory knowledge on Artificial Intelligence. It would come to a great help if you are about to select Artificial Intelligence as a course subject. You can briefly know about the areas of AI in which research is prospering.AudienceThis tutorial is prepared for the students at beginner level who aspire to learn Artificial Intelligence.PrerequisitesThe basic knowledge of Computer Science is mandatory. The knowledge of Mathematics, Languages, Science, Mechanical or Electrical engineering is a plus.Disclaimer & CopyrightCopyright 2015 by Tutorials Point (I) Pvt. Ltd.All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher.We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd. provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. If you discover any errors on our website or in this tutorial,******************************************.T able of ContentsAbout the Tutorial (i)Audience (i)Prerequisites (i)Disclaimer & Copyright (i)Table of Contents ......................................................................................................................................... i i 1.OVERVIEW OF AI . (1)What is Artificial Intelligence? (1)Philosophy of AI (1)Goals of AI (1)What Contributes to AI? (2)Programming Without and With AI (2)What is AI Technique? (3)Applications of AI (3)History of AI (4)2.INTELLIGENT SYSTEMS (6)What is Intelligence? (6)Types of Intelligence (6)What is Intelligence Composed of? (7)Difference between Human and Machine Intelligence (9)3.RESEARCH AREAS OF AI (10)Real Life Applications of Research Areas (11)Task Classification of AI (12)4.AGENTS AND ENVIRONMENTS (14)What are Agent and Environment? (14)Agents Terminology (14)Rationality (15)What is Ideal Rational Agent? (15)The Structure of Intelligent Agents (15)The Nature of Environments (18)Properties of Environment (19)5.POPULAR SEARCH ALGORITHMS (20)Single Agent Pathfinding Problems (20)Search Terminology (20)Brute-Force Search Strategies (20)Informed (Heuristic) Search Strategies (23)Local Search Algorithms (24)6.FUZZY LOGIC SYSTEMS (27)What is Fuzzy Logic? (27)Why Fuzzy Logic? (27)Fuzzy Logic Systems Architecture (28)Example of a Fuzzy Logic System (29)Application Areas of Fuzzy Logic (32)Advantages of FLSs (33)Disadvantages of FLSs (33)7. NATURAL LANGUAGE PROCESSING (34)Components of NLP (34)Difficulties in NLU (34)NLP Terminology (35)Steps in NLP (35)Implementation Aspects of Syntactic Analysis (36)8.EXPERT SYSTEMS (40)What are Expert Systems? (40)Capabilities of Expert Systems (40)Components of Expert Systems (41)Knowledge Base (41)Inference Engine (42)User Interface (43)Expert Systems Limitations (44)Applications of Expert System (44)Expert System Technology (45)Development of Expert Systems: General Steps (45)Benefits of Expert Systems (46)9.ROBOTICS (47)What are Robots? (47)What is Robotics? (47)Difference in Robot System and Other AI Program (47)Robot Locomotion (48)Components of a Robot (50)Computer Vision (50)Tasks of Computer Vision (50)Application Domains of Computer Vision (51)Applications of Robotics (51)10.NEURAL NETWORKS (53)What are Artificial Neural Networks (ANNs)? (53)Basic Structure of ANNs (53)Types of Artificial Neural Networks (54)Working of ANNs (55)Machine Learning in ANNs (55)Bayesian Networks (BN) (56)Applications of Neural Networks (59)11.AI ISSUES (61)12.AI TERMINOLOGY (62)1.Overview of AIArtificial IntelligenceSince the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.What is Artificial Intelligence?According to the father of Artificial Intelligence John McCarthy, it is “The science and engineering o f making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.Philosophy of AIWhile exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?”Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.Goals of AI∙To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.∙To Implement Human Intelligence in Machines:Creating systems that understand, think, learn, and behave like humans.What Contributes to AI?Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.Out of the following areas, one or multiple areas can contribute to build an intelligent system.Programming Without and With AIThe programming without and with AI is different in following ways:Programming Without AI Programming With AIA computer program without AI can answer the specific questions it is meant to solve.A computer program with AI can answer the generic questions it is meant to solve.Modification in the program leads to change in its structure.AI programs can absorb new modifications by putting highly independent pieces of information together. Hence you can modify even a minute piece of information of program without affecting its structure.Modification is not quick and easy. It maylead to affecting the program adversely.Quick and Easy program modification.What is AI T echnique?In the real world, the knowledge has some unwelcomed properties:∙Its volume is huge, next to unimaginable.∙It is not well-organized or well-formatted.∙It keeps changing constantly.AI Technique is a manner to organize and use the knowledge efficiently in such a way that: ∙It should be perceivable by the people who provide it.∙It should be easily modifiable to correct errors.∙It should be useful in many situations though it is incomplete or inaccurate.AI techniques elevate the speed of execution of the complex program it is equipped with.Applications of AIAI has been dominant in various fields such as:∙GamingAI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.∙Natural Language ProcessingIt is possible to interact with the computer that understands natural language spoken by humans.∙Expert SystemsThere are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.∙Vision SystemsThese systems understand, interpret, and comprehend visual input on the computer.For example,o A spying aeroplane takes photographs which are used to figure out spatial information or map of the areas.o Doctors use clinical expert system to diagnose the patient.o Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.∙Speech RecognitionSome intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.∙Handwriting RecognitionThe handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.∙Intelligent RobotsRobots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.History of AIHere is the history of AI during 20th century:Year Milestone / Innovation1923 Karel Čapek’s play named “Rossum's Universal Robots” (RUR) opens in London, first use of the word "robot" in English.1943 Foundations for neural networks laid.1945 Isaac Asimov, a Columbia University alumni, coined the term Robotics.1950 Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search.1956 John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.1958 John McCarthy invents LISP programming language for AI.1964 Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.1965 Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English.1969Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving.Artificial Intelligence1973 The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.1979 The first computer-controlled autonomous vehicle, Stanford Cart, was built.1985Harold Cohen created and demonstrated the drawing program, Aaron.1990 Major advances in all areas of AI:∙Significant demonstrations in machine learning ∙Case-based reasoning∙Multi-agent planning∙Scheduling∙Data mining, Web Crawler∙natural language understanding and translation ∙Vision, Virtual Reality∙Games1997 The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.2000 Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomad explores remote regions of Antarctica and locates meteorites.Artificial Intelligence While studying artificially intelligence, you need to know what intelligence is. This chapter covers Idea of intelligence, types, and components of intelligence.What is Intelligence?The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations. T ypes of IntelligenceAs described by Howard Gardner, an American developmental psychologist, the Intelligence comes in multifold:Intelligence Description ExampleLinguistic intelligence The ability to speak, recognize, and use mechanismsof phonology (speech sounds), syntax (grammar),and semantics (meaning).Narrators, OratorsMusical intelligence The ability to create, communicate with, and understand meanings made of sound, understandingof pitch, rhythm.Musicians,Singers,ComposersLogical-mathematical intelligence The ability of use and understand relationships in theabsence of action or objects. Understanding complexand abstract ideas.Mathematicians,ScientistsSpatial intelligence The ability to perceive visual or spatial information,change it, and re-create visual images withoutreference to the objects, construct 3D images, andto move and rotate them.Map readers,Astronauts,PhysicistsBodily-Kinesthetic intelligence The ability to use complete or part of the body tosolve problems or fashion products, control over fineand coarse motor skills, and manipulate the objects.Players, DancersIntra-personal intelligence The ability to distinguish among one’s own feelings,intentions, and motivations.Gautam Buddha 2.IntelligenT SystemsInterpersonal intelligence The ability to recognize and make distinctions amongother people’s feelings, beliefs, and intentions.MassCommunicators,InterviewersYou can say a machine or a system is artificially intelligent when it is equipped with at least one and at most all intelligences in it.End of ebook previewIf you liked what you saw…Buy it from our store @ https://。