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Identification of neuro-fractional Hammerstein systems: a hybrid frequency-/time-domain approach

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Abstract

In this paper, modeling and identification of nonlinear dynamic systems using neuro-fractional Hammerstein model are considered. The proposed model consists of the neural networks (NNs) as the nonlinear subsystem and the fractional-order state space (FSS) as the linear subsystem. The identification procedure consists of a hybrid frequency-/time-domain approach based on the input–output data acquired from the system. First in the frequency domain, the fractional order and fractional degree of the FSS subsystem are determined offline using an iterative linear optimization algorithm. Then, in the time domain, the state-space matrices of the FSS as well as parameters of the NN are estimated using Lyapunov stability theory. Moreover, in order to use only the input–output data from the system, a fractional-order linear observer based on auxiliary model idea is utilized to estimate the system states. The convergence and stability analysis of the proposed method are provided. Simulating and experimental examples show superior performance of the proposed method as compared with the Hammerstein models reported in the literature.

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Authors and Affiliations

  1. School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran

    Mohammad-Reza Rahmani & Mohammad Farrokhi

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  1. Mohammad-Reza Rahmani
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  2. Mohammad Farrokhi
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Correspondence to Mohammad Farrokhi.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Rahmani, MR., Farrokhi, M. Identification of neuro-fractional Hammerstein systems: a hybrid frequency-/time-domain approach. Soft Comput 22, 8097–8106 (2018). https://doi.org/10.1007/s00500-017-2749-6

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