Type-2 Fuzzy Systems for Improving Training Data and Decision Making in Modular Neural Networks for Image Recognition

In this paper we consider a Modular Neural Network combined with two Interval Type-2 Fuzzy Inference Systems (FIS 2) for image recognition. The first FIS 2 is used for edges detection in training data, and the second one to find the best parameters for the Sugeno Integral as decision operator. Once again Fuzzy Logic is shown to be a tool that can help improve the results of a neural system facilitating the representation of the human perception.