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

Helon Vicente Hultmann Ayala (1) , Didace Habineza (2) , Micky Rakotondrabe (3) , Leandro dos Santos Coelho (4, 5)

1 PUC-Rio - Pontifícia Universidade Católica do Rio de Janeiro [Brasil] = Pontifical Catholic University of Rio de Janeiro [Brazil] = Université catholique pontificale de Rio de Janeiro [Brésil]
2 Punch Powertrain (BELGIUM)
3 LGP - Laboratoire Génie de Production
4 PUCPR - Pontifical Catholic University of Paraná
5 UFPR - Universidade Federal do Paraná [Curitiba, Brasil] = Federal University of Paraná [Curitiba, Brazil] = Université fédérale du Paraná [Curitiba, Brésil]

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.

Mots clés: System identification; Nonlinear systems; Radial basis functions neural networks; Coevolutionary algorithms; Piezoelectric manipulator

Domaines: Robotique [cs.RO]

Helon Vicente Hultmann Ayala, Didace Habineza, Micky Rakotondrabe, Leandro dos Santos Coelho. Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks. Applied Soft Computing, 2020, 87, pp.1-12. ⟨10.1016/j.asoc.2019.105990⟩. ⟨hal-03120205⟩