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A Fuzzy MLP Approach for Nonlinear System Identification

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Abstract

In case of decision making problems, identification of nonlinear systems is an important issue. Identification of nonlinear systems using a multilayer perceptron (MLP) trained with back propagation becomes much complex with an increase in number of input data, number of layers, number of nodes, and number of iterations in computation. In this paper, an attempt has been made to use fuzzy MLP and its learning algorithm for identification of nonlinear system. The fuzzy MLP and its training algorithm which allows to accelerate a process of training, which exceeds in comparison with classical MLP is proposed. Results show a sharp reduction in search for optimal parameters of a neuro fuzzy model as compared to the classical MLP. A training performance comparison has been carried out between MLP and the proposed fuzzy-MLP model. The time and space complexities of the algorithms have been analyzed. It is observed that the number of epochs has sharply reduced and performance increased compared with classical MLP.

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Authors and Affiliations

  1. National University of Uzbekistan named after M. Ulugbek, Tashkent, Uzbekistan

    A. R. Marakhimov & K. K. Khudaybergenov

Authors
  1. A. R. Marakhimov
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  2. K. K. Khudaybergenov
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Correspondence to A. R. Marakhimov.

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Translated from Sovremennaya Matematika. Fundamental’nye Napravleniya (Contemporary Mathematics. Fundamental Directions), Vol. 65, No. 1, Contemporary Problems in Mathematics and Physics, 2019.

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Marakhimov, A.R., Khudaybergenov, K.K. A Fuzzy MLP Approach for Nonlinear System Identification. J Math Sci 265, 43–51 (2022). https://doi.org/10.1007/s10958-022-06043-z

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  • DOI: https://doi.org/10.1007/s10958-022-06043-z

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