Adaptive Nonlinear System Identification

Tokunbo Ogunfunmi

ISBN 10: 0387263284 / ISBN 13: 9780387263281

Editore: Springer US, Springer US Sep 2007, 2007

Riguardo questo articolo

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 248 pp. Englisch.

Codice articolo 9780387263281

Dati bibliografici

• Titolo: Adaptive Nonlinear System Identification
• Editore: Springer US, Springer US Sep 2007
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 0387263284
• ISBN 13: 9780387263281
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 19 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 19 millimetri di profondità
• Peso: 541 grammi

Descrizione articolo

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.