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Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

Hultmann Ayala, Helon Vicente and Habineza, Didace and Rakotondrabe, Micky and Dos Santos Coelho, Leandro Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks. (2020) Applied Soft Computing, 87. 1-12. ISSN 1568-4946

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Official URL: https://doi.org/10.1016/j.asoc.2019.105990

Abstract

The present work deals with the application of coevolutionary algorithms and artificial neural networks to perform input selection and related parameter estimation for nonlinear black-box models in system identification. In order to decouple the resolution of the input selection and parameter estimation, we propose a problem decomposition formulation and solve it by a coevolutionary algorithm strategy. The novel methodology is successfully applied to identify a magnetorheological damper, a continuous polymerization reactor and a piezoelectric robotic micromanipulator. The results show that the method provides valid models in terms of accuracy and statistical properties. The main advantage of the method is the joint input and parameter estimation, towards automating a tedious and error prone procedure with global optimization algorithms.

Item Type:Article
HAL Id:hal-03120205
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Punch Powertrain (BELGIUM)
Other partners > Pontifícia Universidade Católica do Paraná - PUCPR (BRAZIL)
Other partners > Pontifícia Universidade Católica do Rio de Janeiro - PUC (BRAZIL)
Other partners > Universidade Federal do Paraná - UFPR (BRAZIL)
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Deposited On:18 Jan 2021 16:10

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