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Identification of nonlinear discrete systems using a new Hammerstein model with Volterra neural network

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Abstract

For identifying nonlinear discrete systems, a new Hammerstein model consisting of Volterra neural network (VNN) and infinite impulse response (IIR) digital system is developed in this paper. The nonlinear static part of Hammerstein model is the proposed VNN structure that is a combination of the Volterra digital system and feedforward neural network. Another linear dynamic part in the model is expressed by the IIR digital filter. To design weights and thresholds contained in VNN and filter coefficients in IIR, a popular particle swarm optimization (PSO) algorithm is adopted such that the model output is able to approximate the actual nonlinear system output. Finally, many examinations including different population sizes and initial conditions are performed for two nonlinear system examples. All of simulation data including the mean squared error and sum of squared error derived by the proposed method are superior to those by other existing design methods. These sufficiently show the excellent performance of the developed Hammerstein model with PSO tuning on the nonlinear discrete systems modeling.

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Acknowledgements

This work was partially supported by the Ministry of Science and Technology of Taiwan under Grant MOST 108-2221-E-366-003.

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  1. Department of Computer and Communication, Shu-Te University, Kaohsiung, 824, Taiwan

    Wei-Der Chang

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  1. Wei-Der Chang
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Correspondence to Wei-Der Chang.

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Chang, WD. Identification of nonlinear discrete systems using a new Hammerstein model with Volterra neural network. Soft Comput 26, 6765–6775 (2022). https://doi.org/10.1007/s00500-022-07089-6

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