Adaptive recurrent neuro-fuzzy networks based on Takagi-Sugeno inference for nonlinear identification in mechatronic systems

Authors:
Florin Ionescu;Dragos Arotaritei;Stefan Arghir
Affiliations:
HTWG-Konstanz, Konstanz, Germany and Steinbeis Transfer Institute Dynamic Systems, Berlin, Germany;University of Medicine and Pharmacy "Gr. T. Popa", Iasi, Romania;University "Politehnica" of Bucharest, Bucharest, Romania
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Year:
2011

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Abstract

In this paper we propose a recurrent neuro-fuzzy network (RFNN) based on Takagi-Sugeno inference with feedback inside the RFNN for nonlinear identification in mechatronic systems. The parameter optimization of the RFNN is achieved using a differential evolutionary algorithm. The experimental results are analyzed using a study cases modeled in Simulink: the linear power amplifier and the actuator.