1st Edition
Intelligent Systems Modeling, Optimization, and Control
Providing a thorough introduction to the field of soft computing techniques, Intelligent Systems: Modeling, Optimization, and Control covers every major technique in artificial intelligence in a clear and practical style. This book highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. The book demonstrates concepts through simulation examples and practical experimental results. Case studies are also presented from each field to facilitate understanding.
Preface
Acknowledgments
Authors
Intelligent Systems
Introduction
Introduction of Soft Computing Techniques
Summary
References
Modeling of Nonlinear Systems: Fuzzy Logic,
Neural Networks, and Neuro-Fuzzy Systems
Fuzzy Systems
Artificial Neural Networks
Neuro-Fuzzy Systems
Modeling of Dynamic Systems
Conclusions
References
Efficient Training Algorithms
Supervised Algorithm
Unsupervised Algorithm
Backpropagation Algorithm
Dynamic Backpropagation
Orthogonal Least Squares Algorithm
Orthogonal Least Square and Generic Algorithm
Adaptive Least-Squares Learning Using GA
Fuzzy Inverse Model Development
Fuzzy Inverse Model Development
Simulation Examples
Conclusion
References
Model-Based Optimization
Model Building
Model-Based Forward Optimization
Application of Model-Based Optimization Scheme to Grinding Processes
References
Neural Control
Supervised Control
Direct Inverse Control
Model Reference Adaptive Control
Internal Model Control
Model Predictive Control
Feedforward Control
References
Fuzzy Control
Knowledge-Based Fuzzy Control
Model–Based Fuzzy Control
References
Stability Analysis Method
Lyapunov Stability Analysis
Passivity Approach
Conclusion
References
Intelligent Control for SISO Nonlinear Systems
Fuzzy Control System Design
Stability Analysis
Simulation Examples
Implementation—Force Control for Grinding Processes
Simulation and Implementation—Force Control for Milling Processes
Conclusion
References
Intelligent Control for MISO Nonlinear Systems
MLFC-MISO Control System Structure
Stability Analysis
Simulation Examples
Conclusion
References
Knowledge-Based Multivariable Fuzzy Control
Complexity Reduction Methods
Methods to Optimize Multivariable Fuzzy Inferencing Calculation
Multivariable Fuzzy Controller to Deal with the Cross-Coupling Effect
Conclusion
References
Model-Based Multivariable Fuzzy Control
Fuzzy Model of Multivariable Systems
Multivariable Interaction Analysis
Multivariable Fuzzy Control Design
Stability Analysis
Simulation Examples
Conclusion
References
Index
Biography
Yung C. Shin, Chengying Xu
"Coordinating the diversity of their experience, the authors: provide unique and systematic explanations for the modeling of nonlinear systems, optimization, and control of various engineering problems, without depending on mathematical models . . . gives industrial researchers and practitioners a detailed analysis of practical issues in the development of applied intelligent systems . . . Equally useful for graduate students and those familiarizing themselves with the nuances of the field, it uses case studies, simulation examples and practical experimental results throughout to illustrate relevant theory and algorithms."
– In MCEER, 2009
