Adaptive Nonlinear System Identification [electronic resource] : The Volterra and Wiener Model Approaches / by Tokunbo Ogunfunmi.

By: Ogunfunmi, Tokunbo [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Signals And Communication Technology: Publisher: Boston, MA : Springer US, 2007Description: XVI, 232 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387686301Subject(s): Engineering | Computer vision | Telecommunication | Systems engineering | Engineering | Signal, Image and Speech Processing | Control, Robotics, Mechatronics | Image Processing and Computer Vision | Circuits and Systems | Communications Engineering, NetworksAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 LOC classification: TK5102.9TA1637-1638TK7882.S65Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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