Adaptive Nonlinear System Identification

Ogunfunmi, Tokunbo

ISBN 10: 0387263284 / ISBN 13: 9780387263281

Editore: Springer-Verlag New York Inc., 2007

Focuses on System Identification applications of the adaptive methods presented, but which can also be applied to other applications of adaptive nonlinear processes. This title covers research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field. Series: Signals and Communication Technology. Num Pages: 232 pages, biography. BIC Classification: GPFC. Category: (P) Professional & Vocational. Dimension: 243 x 164 x 24. Weight in Grams: 532. . 2007. 2007th Edition. hardcover. . . . . Codice articolo V9780387263281

Riassunto:

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.

Dalla quarta di copertina:

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.

Dati bibliografici

Titolo: Adaptive Nonlinear System Identification
Casa editrice: Springer-Verlag New York Inc.
Data di pubblicazione: 2007
Legatura: Rilegato
Condizione: New