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Genetic Optimization of Type-1, Type-2 and Intuitionistic Fuzzy Recognition Systems

  • Patricia MelinEmail author
Conference paper
  • 8 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1081)

Abstract

In this paper a new method for fuzzy system optimization is presented. The proposed method performs the intuitionistic or type-2 fuzzy inference system design using a hierarchical genetic algorithm as an optimization method. This method is an improvement of a fuzzy system optimization approach presented in previous works where only the optimization of type-1 and interval type-2 fuzzy inference systems was performed considering a human recognition application. Human recognition is performed using three biometric measures namely iris, ear, and voice, where the main idea is to perform the combination of responses in modular neural networks using an optimized fuzzy inference system to improve the final results without and with noisy conditions. The results obtained show the effectiveness of the proposed method for designing optimal structures of fuzzy systems.

Keywords

Modular neural networks Type-1 fuzzy logic Interval type-2 fuzzy logic Intuitionistic fuzzy logic Human recognition Hierarchical genetic algorithm 

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Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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