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Robust Neuroevolutionary Identification of Nonlinear Nonstationary Objects

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Cybernetics and Systems Analysis Aims and scope

Abstract

The neuroevolutionary approach is proposed to construct mathematical models of nonlinear nonstationary objects under non-Gaussian noise. The general structure of an evolutionary feed-forward neural network is considered. The modeling of various cases of nonstationarity has proved the efficiency of the proposed approach.

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Authors and Affiliations

  1. Kharkiv National University of Radioelectronics, Kharkiv, Ukraine

    O. G. Rudenko & O. O. Bezsonov

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  1. O. G. Rudenko
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  2. O. O. Bezsonov
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Correspondence to O. O. Bezsonov.

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Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 21–36, January–February 2014.

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Rudenko, O.G., Bezsonov, O.O. Robust Neuroevolutionary Identification of Nonlinear Nonstationary Objects. Cybern Syst Anal 50, 17–30 (2014). https://doi.org/10.1007/s10559-014-9589-5

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  • DOI: https://doi.org/10.1007/s10559-014-9589-5

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