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Ogunfunmi T. Adaptive Nonlinear System Identification

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Ogunfunmi T. Adaptive Nonlinear System Identification
Springer, 2007. — 232 p. — ISBN-10 0387263284; ISBN-13 978-0387263281.
This book introduces engineers and researchers to the field of nonlinear adaptive system identification. It 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 performances of the two methods are then compared to determine which model performs better for system identification applications. This book is not exhaustive of all the methods of nonlinear adaptive system identification. 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 nonlinear systems and adaptive signal processing.
Preface
Acknowledgements
Introduction to Nonlinear Systems
Polynomial Models of Nonlinear Systems
Volterra and Wiener Nonlinear Models
Nonlinear System Identification Methods
Introduction 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
References
Index
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