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 xiii
... Implementation of REICS 167 5.3.4 Simulations and Applications of REICS 5.4 Summary References Chapter 6 Fuzzy Control Systems 6.1 Mathematical Foundation for Fuzzy Control 174 179 180 182 182 6.1.1 Fuzzy Sets and Their Operations 182 ...
... Implementation of REICS 167 5.3.4 Simulations and Applications of REICS 5.4 Summary References Chapter 6 Fuzzy Control Systems 6.1 Mathematical Foundation for Fuzzy Control 174 179 180 182 182 6.1.1 Fuzzy Sets and Their Operations 182 ...
Page xiv
... Implementation and Results of the Biped Robot Control 236 6.6 Summary 238 References 238 Chapter 7 Neurocontrol Systems 242 7.1 Introduction to Artificial Neural Networks 242 7.1.1 Origins of ANN Research 7.1.2 ANN for Control 7.2 ...
... Implementation and Results of the Biped Robot Control 236 6.6 Summary 238 References 238 Chapter 7 Neurocontrol Systems 242 7.1 Introduction to Artificial Neural Networks 242 7.1.1 Origins of ANN Research 7.1.2 ANN for Control 7.2 ...
Page xvi
... Implementation and Experimental Results 363 9.2 Intelligent Process Supervision and Control - Hierarchically Intelligent Control for Industrial Boiler 367 9.2.1 Structure of Hierarchical Hybrid Controller 368 9.2.2 Algorithms in the ...
... Implementation and Experimental Results 363 9.2 Intelligent Process Supervision and Control - Hierarchically Intelligent Control for Industrial Boiler 367 9.2.1 Structure of Hierarchical Hybrid Controller 368 9.2.2 Algorithms in the ...
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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 |