Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition

Authors:
Olivia Mendoza;Patricia Melin;Oscar Castillo;Guillermo Licea
Affiliations:
Universidad Autonoma de Baja California, Tijuana, Mexico;Tijuana Institute of Technology, Tijuana, Mexico;Tijuana Institute of Technology, Tijuana, Mexico;Universidad Autonoma de Baja California, Tijuana, Mexico
Venue:
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Year:
2007

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Abstract

The combination of Soft Computing techniques allows the improvement of intelligent systems with different hybrid approaches. In this work we consider two parts of a Modular Neural Network for image recognition, where a Type-2 Fuzzy Inference System (FIS 2) makes a great difference. The first FIS 2 is used for feature extraction in training data, and the second one to find the ideal parameters for the integration method of the modular neural network. Once again Fuzzy Logic is shown to be a tool that can help improve the results of a neural system, when facilitating the representation of the human perception.