Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches

ISBN-13: 9781441938831 / Angielski / Miękka / 2010 / 232 str.

Tokunbo Ogunfunmi

Opis

Focuses on System Identification applications of the adaptive methods presented. but which can also be applied to other applications of adaptive nonlinear processes.

Covers recent research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field.

Recenzje

From the reviews:

"In this book, the author presents simple, concise, easy-to-understand methods for identifying nonlinear systems using adaptive filter algorithms well known for linear systems identification. The book focuses on the Volterra and Wiener models for nonlinear systems ... . It is another contribution to the current literature on the subject. The book will be useful for graduate students, engineers and researchers in the area of the nonlinear systems identification and adaptive signal processing." (George S. Stavrakakis, Zentralblatt MATH, Vol. 1130 (8), 2008)

Spis treści

to Nonlinear Systems.- Polynomial Models of Nonlinear Systems.- Volterra and Wiener Nonlinear Models.- Nonlinear System Identification Methods.- to Adaptive Signal Processing.- Nonlinear Adaptive System Identification Based on Volterra Models.- Nonlinear Adaptive System Identification Based on Wiener Models (Part 1).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 2).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 3).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 4).- Conclusions, Recent Results, and New Directions.

Blurb

Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches introduces engineers and researchers to the field of nonlinear adaptive system identification. The book includes recent research results in the area of adaptive nonlinear system identification and presents simple, concise, easy-to-understand methods for identifying nonlinear systems. These methods use adaptive filter algorithms that are well known for linear systems identification. They are applicable for nonlinear systems that can be efficiently modeled by polynomials.

After a brief introduction to nonlinear systems and to adaptive system identification, the author presents the discrete Volterra model approach. This is followed by an explanation of the Wiener model approach. Adaptive algorithms using both models are developed. The performance of the two methods are then compared to determine which model performs better for system identification applications.

Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches is useful to graduates students, engineers and researchers in the areas of nonlinear systems, control, biomedical systems and in adaptive signal processing.