The book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. Additionally, it provides the...
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En magasin
Résumé
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 modem 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.
Sommaire
OPTIMIZATION TECHNIQUES
Introduction to Optimization
Linear Optimization
Nonlinear Local Optimization
Nonlinear Global Optimization
Unsupervised Learning Techniques
Model Complexity Optimization
STATIC MODELS
Introduction
Linear, Polynomial, and Look-Up Table Models
Neural Networks
Fuzzy and Neuro-Fuzzy Models
Local Linear Neuro-Fuzzy Models : Fundamentals
Local Linear Neuro-Fuzzy Models : Advanced Aspects