TY  - BOOK
AU  - Ogunfunmi,Tokunbo
ED  - SpringerLink (Online service)
TI  - Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches 
T2  - Signals And Communication Technology,
SN  - 9780387686301
AV  - TK5102.9 
U1  - 621.382 23
PY  - 2007///
CY  - Boston, MA
PB  - Springer US
KW  - Engineering
KW  - Computer vision
KW  - Telecommunication
KW  - Systems engineering
KW  - Signal, Image and Speech Processing
KW  - Control, Robotics, Mechatronics
KW  - Image Processing and Computer Vision
KW  - Circuits and Systems
KW  - Communications Engineering, Networks
N1  - to Nonlinear Systems -- Polynomial Models of Nonlinear Systems -- Volterra and Wiener Nonlinear Models -- Nonlinear System Identification Methods -- 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-0-387-68630-1
ER  -