Nelles O. Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models

Springer, 2001. — 785 p.The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications.Optimization Techniques
Introduction to Optimization
Linear Optimization
Nonlinear Local Optimization
Nonlinear Global Optimization
Unsupervised Learning Techniques
Model Complexity Optimization
Summary of Part I
Static Models
Introduction to Static Models
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
Summary of Part II
Dynamic Models
Linear Dynamic System Identification
Nonlinear Dynamic System Identification
Classical Polynomial Approaches
Dynamic Neural and Fuzzy Models
Dynamic Local Linear Neuro-Fuzzy Models
Neural Networks with Internal Dynamics
Applications
Applications of Static Models
Applications of Dynamic Models
Applications of Advanced Methods
A: Vectors and Matrices
B: Statistics