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 vii
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Page xi
... Neural Network - Based Control 1.5.5 Intelligent Scheduling 1.5.6 Voice Control 1.5.7 Control of Restorative Artificial Limbs Intellectualized Instruments vii 1 1 2 3 4 8 8 9 10 11 12 13 17 20 20 21 22 24 24 25 26 26 27 27 2220 28 29 ...
... Neural Network - Based Control 1.5.5 Intelligent Scheduling 1.5.6 Voice Control 1.5.7 Control of Restorative Artificial Limbs Intellectualized Instruments vii 1 1 2 3 4 8 8 9 10 11 12 13 17 20 20 21 22 24 24 25 26 26 27 27 2220 28 29 ...
Page xiv
... Neural Networks 242 7.1.1 Origins of ANN Research 7.1.2 ANN for Control 7.2 Structures of Artificial Neural Networks 7.2.1 The Neuron and Its Properties 7.2.2 The Basic Types of ANN 242 243 244 244 245 7.2.3 Typical Models of ANN 247 ...
... Neural Networks 242 7.1.1 Origins of ANN Research 7.1.2 ANN for Control 7.2 Structures of Artificial Neural Networks 7.2.1 The Neuron and Its Properties 7.2.2 The Basic Types of ANN 242 243 244 244 245 7.2.3 Typical Models of ANN 247 ...
Page xv
... Neural Network 7.5.2 Fuzzy Neural Control Schemes 7.6 Pradigms of NN - Based Control Systems 275 276 279 282 7.6.1 Double - Neuron Syncho Control System for Hydraulic Turbine Generator 283 7.6.2 Direct Fuzzy Neurocontrol for Train ...
... Neural Network 7.5.2 Fuzzy Neural Control Schemes 7.6 Pradigms of NN - Based Control Systems 275 276 279 282 7.6.1 Double - Neuron Syncho Control System for Hydraulic Turbine Generator 283 7.6.2 Direct Fuzzy Neurocontrol for Train ...
Page xvi
... Neural Network Baseline Problem for Control of Aircraft Flare and Touchdown 407 9.5.1 Introduction to Aircraft Flight Control 407 9.5.2 Design Issues of Neural Network Controller 409 9.5.3 Applications and Implementation of Neural ...
... Neural Network Baseline Problem for Control of Aircraft Flare and Touchdown 407 9.5.1 Introduction to Aircraft Flight Control 407 9.5.2 Design Issues of Neural Network Controller 409 9.5.3 Applications and Implementation of Neural ...
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 |