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Adaptive recurrent neuro-fuzzy networks based on takagi-sugeno inference for nonlinear identification in mechatronic systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2011) 6881 LNAI(PART 1) 1-10
DOI: 10.1007/978-3-642-23851-2_1
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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. © 2011 Springer-Verlag.

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Ionescu, F., Arotaritei, D., & Arghir, S. (2011). Adaptive recurrent neuro-fuzzy networks based on takagi-sugeno inference for nonlinear identification in mechatronic systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6881 LNAI, pp. 1–10). https://doi.org/10.1007/978-3-642-23851-2_1

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