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 14
... signals , as well as to make optimization , decision , and motion . The information . view has become a necessary resource for many disciplinary fields in the modern science and technology [ 66 , 75 ] . Let us look at a scientific ...
... signals , as well as to make optimization , decision , and motion . The information . view has become a necessary resource for many disciplinary fields in the modern science and technology [ 66 , 75 ] . Let us look at a scientific ...
Page 17
... ( signal ) identification and processing subsystem [ 9 ] . The former involves intelligent database and reasoning machine , and the latter may be measures and processors for different signals . In the two examples , the information ...
... ( signal ) identification and processing subsystem [ 9 ] . The former involves intelligent database and reasoning machine , and the latter may be measures and processors for different signals . In the two examples , the information ...
Page 19
... Signal Optimization Planning & Reasoning Signal Preprocessing Decision & Coordination Signal Identification Traditional control HH 3 Sensors Actuators 2 Events Actions Process ( plant ) and Environment ( world ) Figure 1.9 . Typical ...
... Signal Optimization Planning & Reasoning Signal Preprocessing Decision & Coordination Signal Identification Traditional control HH 3 Sensors Actuators 2 Events Actions Process ( plant ) and Environment ( world ) Figure 1.9 . Typical ...
Page 24
... signal , and learning a mapping between an input and an output space and synthesizing an associative memory [ 23 ] . The NN techniques has been widely used to information processing , such as pattern recognition , image identification ...
... signal , and learning a mapping between an input and an output space and synthesizing an associative memory [ 23 ] . The NN techniques has been widely used to information processing , such as pattern recognition , image identification ...
Page 37
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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