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Adaptive Recurrent Neuro-Fuzzy Networks Based on Takagi-Sugeno Inference for Nonlinear Identification in Mechatronic Systems

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Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6881))

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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.

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Author information

Authors and Affiliations

  1. HTWG-Konstanz, D-78462, Konstanz, Germany

    Florin Ionescu

  2. Steinbeis Transfer Institute Dynamic Systems, D-10247, Berlin, Germany

    Florin Ionescu

  3. University of Medicine and Pharmacy “Gr. T. Popa”, RO-700115, Iasi, Romania

    Dragos Arotaritei

  4. University “Politehnica” of Bucharest, RO-060042, Bucharest, Romania

    Stefan Arghir

Authors
  1. Florin Ionescu
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  2. Dragos Arotaritei
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  3. Stefan Arghir
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Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of omputer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Mawson Lakes, 5095, Adelaide, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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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: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_1

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  • DOI: https://doi.org/10.1007/978-3-642-23851-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23850-5

  • Online ISBN: 978-3-642-23851-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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