Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches (Signals and Communication Technology) - Hardcover

Über die Autorin bzw. den Autor

Thomas K. Paul has worked in signal communications since 1996. Most recently, he has worked at ICR Inc. and Northrop Grumman Corp, in support of government defense contracts. Prior to that, at Ralink Technology, he worked on development of a cost-reduced version of the 802.11b WLAN chipset. And earlier, at PC-TEL Inc., he worked on the maintenance and improvement of their V.92 software modem product. He also holds a U.S. Patent regarding dynamic block processing in soft-ware modems. He earned the B.A.Sc. degree at the University of Toronto, Canada in 1996, and M.S.E.E. and Ph.D. degrees at Santa Clara University, Santa Clara, California in 2007 and 2013, respectively. His research interests are in communica-tions system design and digital signal processing. Tokunbo Ogunfunmi received the B.S. (first class honors) degree from Obafemi Awolowo University, Nigeria, and the M.S. and Ph.D. degrees from Stanford University, Stanford, California, all in Electrical Engineering. He is currently a Professor of Electrical and Computer Engineering and Director of the Information Processing and Machine Learning Research Laboratory at Santa Clara University (SCU), Santa Clara, California. From 2010-2014, he served as the Associate Dean for Research and Faculty Development for the SCU School of Engineering. At SCU, he teaches a variety of courses in circuits, systems, signal processing and related areas. His current research interests include machine learning, deep learning, speech and multimedia (audio, video) compression, digital and adaptive signal processing and applications and nonlinear signal processing. He has published three books and over 250 refereed journal and conference papers in these areas.

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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.