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Intuitionistic neuro-fuzzy network with evolutionary adaptation

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Abstract

Intuitionistic fuzzy inference systems (IFISs) incorporate imprecision in the construction of membership functions present in fuzzy inference systems. In this paper we design intuitionistic neuro-fuzzy networks to adapt the antecedent and consequent parameters of IFISs. We also propose a mean of maximum defuzzification method for a class of Takagi–Sugeno IFISs and this method is compared with the basic defuzzification distribution operator. On both real-life credit scoring data and seven benchmark regression datasets we show that the intuitionistic neuro-fuzzy network trained with particle swarm optimization outperforms traditional ANFIS methods (hybrid and backpropagation) and ANFIS trained with evolutionary algorithms (genetic algorithm and particle swarm optimization), respectively. A set of nonparametric tests for multiple datasets is performed to demonstrate statistical differences between the algorithms. In the task of adapting the intuitionistic neuro-fuzzy network, we show that particle swarm optimization provides a higher prediction accuracy compared with traditional algorithms based on gradient descent or least-squares estimation.

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Acknowledgments

This work was supported by the scientific research project of the Czech Sciences Foundation Grant No: 13-10331S.

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Authors and Affiliations

  1. Faculty of Economics and Administration, Institute of System Engineering and Informatics, University of Pardubice, Studentska 95, Pardubice, 532 10, Czech Republic

    Petr Hájek & Vladimír Olej

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  1. Petr Hájek
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  2. Vladimír Olej
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Correspondence to Petr Hájek.

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Hájek, P., Olej, V. Intuitionistic neuro-fuzzy network with evolutionary adaptation. Evolving Systems 8, 35–47 (2017). https://doi.org/10.1007/s12530-016-9157-5

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