Fractional order neural networks for system identification

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

Chaos, Solitons & Fractals, 2020, vol. 130, issue C

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.

Keywords: Fractional calculus; Neural networks; Black box modeling; System identification (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:130:y:2020:i:c:s096007791930390x

DOI: 10.1016/j.chaos.2019.109444

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