Fractional Hammerstein system identification based on two decomposition principles

Karima Hammar; Tounsia Djamah; Maamar Bettayeb

doi:10.1016/j.ifacol.2019.11.106

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Fractional Hammerstein system identification based on two decomposition principles

Conference proceeding

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Fractional Hammerstein system identification based on two decomposition principles

Karima Hammar, Tounsia Djamah and Maamar Bettayeb

IFAC PAPERSONLINE, Vol.52(13), pp.206-210

01/01/2019

DOI: https://doi.org/10.1016/j.ifacol.2019.11.106

Abstract

Automation & Control Systems

Science & Technology

Technology

This paper deals with fractional non-linear system identification, where the Hammerstein Controlled Auto-Regression (HCAR) model is considered. The identification process is derived for the regression form of the HCAR system based on the Over-parametrization principle and the Key-term separation principle. Levenberg-Marquardt algorithm combined with each of these principles is developed to identify the fractional HCAR system. Various simulations test the efficiency of the optimization method based on these principles. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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Title: Fractional Hammerstein system identification based on two decomposition principles
Creators - without role: Karima Hammar - Mouloud Mammeri University of Tizi-Ouzou
Tounsia Djamah - Mouloud Mammeri University of Tizi-Ouzou
Maamar Bettayeb - University of Sharjah
Publication Details: IFAC PAPERSONLINE, Vol.52(13), pp.206-210
Publisher: Elsevier
Number of pages: 5
Identifiers: 9936566708331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Conference proceeding