Home Nonlinear System Identification Using Laguerre Wavelet Models
Article
Licensed
Unlicensed Requires Authentication

Nonlinear System Identification Using Laguerre Wavelet Models

  • P. Aadaleesan and Prabirkumar Saha
Published/Copyright: June 19, 2008
Become an author with De Gruyter Brill

A compact and efficient model that is capable of approximating both the linear and nonlinear components of the process is in high demand. In this paper, a novel black-box modeling technique viz. Wiener type Laguerre-Wavelet model is proposed. The Laguerre-Wavelet model has the capability to approximate a function with moderate/reasonable number of data with appreciable approximation accuracy. The Laguerre filter is used to approximate the linear dynamic components of the process, whereas wavelet structure is used for the static nonlinear components. The ability of wavelets to approximate any square-integrable function to any arbitrary precision by input-output mappings is utilised for the nonlinear approximation following a modified single scaling method. The performance efficiency of the proposed Wiener type model structure, Laguerre-Wavelet model, is demonstrated using simulation case study on a continuous bioreactor.

Published Online: 2008-6-19

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

Downloaded on 30.11.2025 from https://www.degruyterbrill.com/document/doi/10.2202/1934-2659.1136/pdf
Scroll to top button