Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches (Signals and Communication Technology)
Language: English
Published by Springer, 2007
Series: Book 18 of 180 - Signals and Communication Technology
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
Condition: New
US$ 135.18
Quantity: Over 20 available
Add to basketItem description from seller
Seller Inventory # ria9780387263281_new
- Title
- Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches (Signals and Communication Technology)
- Author
- Ogunfunmi, Tokunbo
- Publisher
- Springer
- Publication year
- 2007
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0387263284
- ISBN 13
- 9780387263281
- Series
- Book 18 of 180: Signals and Communication Technology
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Ria Christie Collections
Uxbridge, United Kingdom
AbeBooks seller since March 25, 2015
Shipping rates from United Kingdom to U.S.A.
| Item | 6 to 12 business days | 6 to 12 business days |
|---|---|---|
| First item | US$ 16.23 | US$ 16.23 |
Payment methods
Store description
Specialty
Educational books, Textbooks, Fiction, Non- fictionSeller's business information
Ryefield Investments Limited
175 Pield Heath Road
Uxbridge, United Kingdom UB8 3NL
Terms of sale
All Returns and Refund are as per Abebooks policies.
Shipping terms
Orders usually ship within 2 business days. If your book order is heavy or oversized, we may contact you to let you know extra shipping is required. Thank you!