Abstract
This book consists of 13 chapters contributed mainly by European academic authors. The book opens with three overview chapters on fuzzy, neural, and evolutionary systems for system identification and control. Later chapters cover such topics as adaptive local linear modeling and control of nonlinear dynamical systems; Gaussian process approaches to nonlinear modeling and control; neuro-fuzzy model construction, design and estimation; and reinforcement learning for on-line control and optimization. The last few chapters concentrate on specific application areas, such as diagnostics, autonomous parking, and medicine. The book provides decent coverage of computational intelligence methods relevant to modeling and control. Better chapter cross referencing and proofreading would have benefited the book. Despite its shortcomings, the book is recommended to a broad audience interested in CI applications to system identification and control.
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