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 viii
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Page xi
... Control Advance in Intelligent Control 1.2 1.2.1 Automation and Artificial ... Expert Control and Fuzzy Control Systems 1.5.4 Neural Network - Based ... System 333333 35 35 36 37 2.1.3 Computer Simulation of Human Intelligence 38 ...
... Control Advance in Intelligent Control 1.2 1.2.1 Automation and Artificial ... Expert Control and Fuzzy Control Systems 1.5.4 Neural Network - Based ... System 333333 35 35 36 37 2.1.3 Computer Simulation of Human Intelligence 38 ...
Page xiii
... Control Systems 4.3.1 Hierarchically Robotic Assembly System 103 115 115 116 121 123 124 4.3.2 Hierarchical Control of A Nuclear Reactor 129 4.4 Summary References Chapter 5 Expert Control Systems 5.1 Expert Sytems 134 135 139 139 5.1.1 ...
... Control Systems 4.3.1 Hierarchically Robotic Assembly System 103 115 115 116 121 123 124 4.3.2 Hierarchical Control of A Nuclear Reactor 129 4.4 Summary References Chapter 5 Expert Control Systems 5.1 Expert Sytems 134 135 139 139 5.1.1 ...
Page xiv
... Expert Fuzzy Controller 201 6.3 Design of Fuzzy Controllers 202 6.3.1 Design Requirements for Fuzzy Controllers 202 ... Control Systems 221 6.4.4 Robustness of Fuzzy Control Systems 223 6.4.5 Controllability and Robustness of a Fuzzy Control ...
... Expert Fuzzy Controller 201 6.3 Design of Fuzzy Controllers 202 6.3.1 Design Requirements for Fuzzy Controllers 202 ... Control Systems 221 6.4.4 Robustness of Fuzzy Control Systems 223 6.4.5 Controllability and Robustness of a Fuzzy Control ...
Page xv
... Control 7.4.6 NN - Based Adaptive Judgement Control 7.4.7 CMAC Based Control 263 263 264 267 267 269 269 7.4.8 Multilayered NN Control 7.4.9 Hierarchical NN Control 271 273 7.5 Integration of Fuzzy Logic , Expert System and NN for Control ...
... Control 7.4.6 NN - Based Adaptive Judgement Control 7.4.7 CMAC Based Control 263 263 264 267 267 269 269 7.4.8 Multilayered NN Control 7.4.9 Hierarchical NN Control 271 273 7.5 Integration of Fuzzy Logic , Expert System and NN for Control ...
Contents
1 | 1 |
2 | 21 |
9 | 27 |
Methodology of Knowledge Representation | 35 |
References | 62 |
3 | 69 |
Inference under Uncertainty | 82 |
References | 135 |
Neurocontrol Systems | 242 |
888888 | 276 |
References | 296 |
87 | 301 |
Learning Control Systems | 302 |
References | 347 |
Intelligent Control Systems in Application | 353 |
427 | |
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Common terms and phrases
adaptive control application architecture artificial intelligence automatic control blackboard closed-loop CMAC cognitive complex components control process control rules control strategy controlled object coordination level defined Definition defuzzification developed dynamic Equation error example execution expert control system expert system fault feedback feedforward fuzzy control system fuzzy logic fuzzy logic controller fuzzy relation fuzzy rules fuzzy sets fuzzy system G. N. Saridis genetic algorithm goal graph heuristic human IEEE IEEE Trans implementation inference engine intelligent control system intelligent machines interface iterative learning control knowledge base knowledge representation knowledge-based layer learning algorithm learning control system linguistic mapping membership function method module neural network neurocontrol neuron NN-based node nonlinear on-line operation optimal organization level output neuron parameters performance PID controller planning plant problem Proc reasoning REICS represent self-learning shown in Figure signal simulation solved structure supervised learning task techniques theory variables
References to this book
Multisensor Fusion: A Minimal Representation Framework Rajive Joshi,Arthur C. Sanderson Limited preview - 1999 |