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Intuitionistic Fuzzy Neural Network: The Case of Credit Scoring Using Text Information

  • Petr HájekEmail author
  • Vladimír Olej
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)

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 fuzzy neural 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 center of area and basic defuzzification distribution operator. On credit scoring data, we show that the intuitionistic fuzzy neural network trained with gradient descent and Kalman filter algorithms outperforms the traditional ANFIS method.

Keywords

ANFIS Intuitionistic fuzzy sets Intuitionistic fuzzy inference systems of Takagi-Sugeno type Intuitionistic fuzzy neural networks Defuzzification methods 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of System Engineering and Informatics, Faculty of Economics and AdministrationUniversity of PardubicePardubiceCzech Republic

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