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. |
From inside the book
Results 1-5 of 59
Page xiii
... Types of Expert Systems 141 5.1.3 Step for Building Expert Systems 143 5.1.4 Model - Based Expert Systems 145 5.2 Expert Control Systems 147 Control Requirements and Design Principle of Expert Control Systems 148 5.2.2 Structures of ...
... Types of Expert Systems 141 5.1.3 Step for Building Expert Systems 143 5.1.4 Model - Based Expert Systems 145 5.2 Expert Control Systems 147 Control Requirements and Design Principle of Expert Control Systems 148 5.2.2 Structures of ...
Page xiv
... Types of ANN 242 243 244 244 245 7.2.3 Typical Models of ANN 247 7.3 Examples of ANN 249 7.3.1 Multilayer Perceptron 250 7.3.2 Group Method of Data Handling Network 252 7.3.3 Adaptive Resonance Theory Network 253 7.3.4 Learning Vector ...
... Types of ANN 242 243 244 244 245 7.2.3 Typical Models of ANN 247 7.3 Examples of ANN 249 7.3.1 Multilayer Perceptron 250 7.3.2 Group Method of Data Handling Network 252 7.3.3 Adaptive Resonance Theory Network 253 7.3.4 Learning Vector ...
Page xvi
... Types of Fault Diagnosis 390 9.4.2 Fault Diagnostics for Space Station Thermal Control Systems 9.4.3 Radar Fault Diagnosis Based on Expert System 391 398 9.5 Intelligent Control for Aircraft Flight and Landing - A Neural Network ...
... Types of Fault Diagnosis 390 9.4.2 Fault Diagnostics for Space Station Thermal Control Systems 9.4.3 Radar Fault Diagnosis Based on Expert System 391 398 9.5 Intelligent Control for Aircraft Flight and Landing - A Neural Network ...
Page 24
Sorry, this page's content is restricted.
Sorry, this page's content is restricted.
Page 45
Sorry, this page's content is restricted.
Sorry, this page's content is restricted.
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 | |
Other editions - View all
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 |