Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach

✍ Scribed by Andrzej Janczak (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2005
• Tongue: English
• Leaves: 207
• Series: Lecture Notes in Control and Information Science 310
• Edition: 1
• Category: Library

✦ Synopsis

This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.

✦ Table of Contents

1 Introduction....Pages 1-30
2 Neural network Wiener models....Pages 31-75
3 Neural network Hammerstein models....Pages 77-116
4 Polynomial Wiener models....Pages 117-141
5 Polynomial Hammerstein models....Pages 143-157
6 Applications....Pages 159-185

✦ Subjects

Control Engineering; Vibration, Dynamical Systems, Control; Systems Theory, Control; Complexity