Evolving Systems

, Volume 8, Issue 1, pp 35–47

Intuitionistic neuro-fuzzy network with evolutionary adaptation

Original Paper

DOI: 10.1007/s12530-016-9157-5

Cite this article as:
Hájek, P. & Olej, V. Evolving Systems (2017) 8: 35. doi:10.1007/s12530-016-9157-5
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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.

Keywords

ANFIS Intuitionistic fuzzy sets Intuitionistic fuzzy inference systems of Takagi–Sugeno type Intuitionistic neuro-fuzzy network Defuzzification method Particle swarm optimization 

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Faculty of Economics and Administration, Institute of System Engineering and InformaticsUniversity of PardubicePardubiceCzech Republic