Intelligent Control: Principles, Techniques And ApplicationsThis book introduces the development process, structural theories and research areas of intelligent control; explains the knowledge representations, searching and reasoning mechanisms as the fundamental techniques of intelligent control; studies the theoretical principles and architectures of various intelligent control systems; analyzes the paradigms of representative applications of intelligent control; and discusses the research and development trends of the intelligent control.From the general point of view, this book possesses the following features: updated research results both in theory and application that reflect the latest advances in intelligent control; closed connection between theory and practice that enables readers to use the principles to their case studies and practical projects; and comprehensive materials that helps readers in understanding and learning. |
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Page xi
... Graph Notion of State 112 2 3 4 8 8 9 10 11 12 13 17 20 20 21 22 24 24 25 26 26 27 2220 27 28 29 42 44 £ £ £ £ 8 w w w w 35 35 36 37 38 40 42 2.3 2.4 Problem Reduction Representation 2.3.1 Problem Reduction Description 2.3.2 CONTENTS.
... Graph Notion of State 112 2 3 4 8 8 9 10 11 12 13 17 20 20 21 22 24 24 25 26 26 27 2220 27 28 29 42 44 £ £ £ £ 8 w w w w 35 35 36 37 38 40 42 2.3 2.4 Problem Reduction Representation 2.3.1 Problem Reduction Description 2.3.2 CONTENTS.
Page xii
... Graph Representation Predicate Logic Representation 2.4.1 Predicate Calculus 46 46 48 50 51 2.4.2 Predicate Formulae 54 2.4.3 Substitution and Unification 2.5 Semantic Network Representation 55 56 2.5.1 Representation of Two - Element ...
... Graph Representation Predicate Logic Representation 2.4.1 Predicate Calculus 46 46 48 50 51 2.4.2 Predicate Formulae 54 2.4.3 Substitution and Unification 2.5 Semantic Network Representation 55 56 2.5.1 Representation of Two - Element ...
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... graph containing nodes such that the path visits each of the nodes precisely once . In most problems of this type , the domain of possible combinations or sequences from which to choose an answer is very large . Usually , Many ...
... graph containing nodes such that the path visits each of the nodes precisely once . In most problems of this type , the domain of possible combinations or sequences from which to choose an answer is very large . Usually , Many ...
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... graph representation of assemebly plans , " AAAI Proc . of the 5th National Conf . on AI , 1986 : 1113-1119 . 41. S. H. Hopkin and P. J. Drazan , " Semiautonomous systems in automatic assembly , " IEE Control Theory and Application ...
... graph representation of assemebly plans , " AAAI Proc . of the 5th National Conf . on AI , 1986 : 1113-1119 . 41. S. H. Hopkin and P. J. Drazan , " Semiautonomous systems in automatic assembly , " IEE Control Theory and Application ...
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Contents
1 | |
CHAPTER 2 METHODOLOGIES OF KNOWLEDGE REPRESENTATION | 35 |
CHAPTER 3 GENERAL INFERENCE PRINCIPLES | 64 |
CHAPTER 4 HIERARCHICAL CONTROL SYSTEMS | 87 |
CHAPTER 5 EXPERT CONTROL SYSTEMS | 139 |
CHAPTER 6 FUZZY CONTROL SYSTEMS | 182 |
CHAPTER 7 NEUROCONTROL SYSTEMS | 242 |
CHAPTER 8 LEARNING CONTROL SYSTEM | 302 |
CHAPTER 9 INTELLIGENT CONTROL SYSTEMS IN APPLICATIONS | 353 |
CHAPTER 10 PROSPECTTVES OF INTELLGENT CONTROL | 432 |
BIOGRAPHY | 441 |
SUBJECT INDEX | 443 |
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Common terms and phrases
activities adaptive control application architecture artificial intelligence automatic control Automation CMAC cognitive complex components control process control rules control strategy coordination level defined Definition defuzzification diagnosis dynamic entropy Equation error example expert control system expert system fault feedback fuzzy control system fuzzy logic fuzzy relation fuzzy sets fuzzy system genetic algorithm goal graph heuristic hierarchical intelligent control human hybrid IEEE IEEE Trans implemented inference engine input intelligent control system intelligent machines interface isoflurane iterative learning control knowledge base knowledge representation knowledge-based layer learning algorithm learning control system linguistic measurement membership function method module neural network neurocontrol neuron NN-based node nonlinear on-line operation optimal organization level parameters performance Petri net PID controller planning plant Proc procedure real-time reasoning repetitive events represent robotic systems semantic network sensors shown in Figure signal simulation solved structure symbol task techniques variables vector