Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
Publisher: Springer | ISBN: 3540673695 | edition 2000 | PDF | 785 pages | 105,2 mb
Publisher: Springer | ISBN: 3540673695 | edition 2000 | PDF | 785 pages | 105,2 mb
The book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. Additionally, it provides the reader with the necessary background on optimization techniques making the book self-contained. The emphasis is put on modern methods based on neural networks and fuzzy systems without neglecting the classical approaches. The entire book is written from an engineering point-of-view, focusing on the intuitive understanding of the basic relationships. This is supported by many illustrative figures. Advanced mathematics is avoided. Thus, the book is suitable for last year undergraduate and graduate courses as well as research and development engineers in industries.