Description
Book SynopsisThis book describes the use of neural networks and fuzzy methods for identifying and controlling nonlinear dynamical systems. It combines advanced concepts from traditional control theory with the intuitive properties of intelligent systems to solve real-world control problems.
Trade Review"…well-organized…very useful for a graduate level control or intelligent systems course…" (
International Journal of Robust and Nonlinear Control, January 2005)
“…the text is well organised with topics judiciously selected to build on each other…the discussion and motivations are rigorous…” (International Journal of Robust & Nonlinear Control, Vol.15, No.1, 10th January 2005)
"...this is an excellent book. It is pedagogically sound and, hence, suitable as a text for graduate courses.... I recommend it also as a very valuable resource to practitioners..." (International Journal of General Systems, Vol. 32, 2003)
Table of ContentsIntroduction.
PART I: FOUNDATIONS.
Mathematical Foundations.
Neural Networks and Fuzzy Systems.
Optimization for Training Approximators.
Function Approximation.
PART II: STATE-FEEDBACK CONTROL.
Control of Nonlinear Systems.
Direct Adaptive Control.
Indirect Adaptive Control.
Implementations and Comparative Studies.
PART III:OUTPUT-FEEDBACK CONTROL.
Output-Feedback Control.
Adaptive Output Feedback Control.
Applications.
PART IV: EXTENSIONS.
Discrete-Time Systems.
Decentralized Systems.
Perspectives on Intelligent Adaptive Systems.
For Further Study.
Bibliography.
Index.
