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Fractional order neural networks for system identification

Author

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  • Zuñiga Aguilar, C.J.
  • Gómez-Aguilar, J.F.
  • Alvarado-Martínez, V.M.
  • Romero-Ugalde, H.M.

Abstract

Neural networks and fractional order calculus have shown to be powerful tools for system identification. In this paper we combine both approaches to propose a fractional order neural network (FONN) for system identification. The learning algorithm was generalized considering the Grünwald-Letnikov fractional derivative. This new black box modeling approach is validated by the identification of three different systems (two benchmark systems and a real system). Comparisons vs others approaches showed that the proposed FONN model reached better accuracy with less number of parameters.

Suggested Citation

  • Zuñiga Aguilar, C.J. & Gómez-Aguilar, J.F. & Alvarado-Martínez, V.M. & Romero-Ugalde, H.M., 2020. "Fractional order neural networks for system identification," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    • Handle: RePEc:eee:chsofr:v:130:y:2020:i:c:s096007791930390x
    DOI: 10.1016/j.chaos.2019.109444
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    8. Rui Luo & Zhinan Peng & Jiangping Hu, 2023. "On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
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