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Adaptive Method of Hybrid Learning for an Evolving Neuro-Fuzzy System

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Cybernetics and Systems Analysis Aims and scope

Abstract

A learning method for neuro-fuzzy systems is proposed that includes processes of architecture evolution, membership function self-learning, and synaptic weights adjustment. This method provides fast response and on-line information processing with the simultaneous adaptation of the system structure and parameters to problem conditions.

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Authors and Affiliations

  1. Problem Research Laboratory of ACS of the Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Ye. V. Bodyanskiy & I. P. Pliss

  2. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    O. O. Boiko

Authors
  1. Ye. V. Bodyanskiy
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  2. O. O. Boiko
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  3. I. P. Pliss
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Corresponding author

Correspondence to Ye. V. Bodyanskiy.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 13–18, July–August, 2015.

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Bodyanskiy, Y.V., Boiko, O.O. & Pliss, I.P. Adaptive Method of Hybrid Learning for an Evolving Neuro-Fuzzy System. Cybern Syst Anal 51, 500–505 (2015). https://doi.org/10.1007/s10559-015-9741-x

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  • DOI: https://doi.org/10.1007/s10559-015-9741-x

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