Modular Neural Network Preprocessing Procedure with Intuitionistic Fuzzy InterCriteria Analysis Method

  • Sotir Sotirov
  • Evdokia Sotirova
  • Patricia Melin
  • Oscar Castilo
  • Krassimir Atanassov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 400)

Abstract

Modular neural networks (MNN) are a tool that can be used for object recognition and identification. Usually the inputs of the MNN can be fed with independent data. However, there are certain limits when we may use MNN, and the number of the neurons is one of the major parameters during the implementation of the MNN. On the other hand, the greater number of neurons slows down the learning process. In the paper, we propose a method for removing the number of the inputs and, hence, the neurons, without removing the error between the target value and the real value obtained on the output of the MNN’s exit. The method uses the recently proposed approach of InterCriteria Analysis, based on index matrices and intuitionistic fuzzy sets, which aims to detect possible correlations between pairs of criteria. The coefficients of the positive and negative consonance can be combined for obtaining the best results and smaller number of the weight coefficients of the neural network.

Keywords

Modular neural network InterCriteria analysis Intuitionistic fuzziness 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sotir Sotirov
    • 1
  • Evdokia Sotirova
    • 1
  • Patricia Melin
    • 2
  • Oscar Castilo
    • 2
  • Krassimir Atanassov
    • 1
    • 3
  1. 1.Intelligent Systems Laboratory“Prof. Dr. Asen Zlatarov” UniversityBurgasBulgaria
  2. 2.Tijuana Institute of TechnologyTijuanaMéxico
  3. 3.Bioinformatics and Mathematical Modelling DepartmentIBPhBME – Bulgarian Academy of SciencesSofiaBulgaria

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