Modular neural networks optimization with hierarchical genetic algorithms with fuzzy response integration for pattern recognition

Abstract

In this paper a new model of a Modular Neural Network (MNN) with fuzzy integration based on granular computing is proposed. The topology and parameters of the MNN are optimized with a Hierarchical Genetic Algorithm (HGA). The proposed method can divide the data automatically into sub modules or granules, chooses the percentage of images and selects which images will be used for training. The responses of each sub module are combined using a fuzzy integrator, the number of the fuzzy integrators will depend of the number of sub modules or granules that the MNN has at a particular moment. The method was applied to the case of human recognition to illustrate its applicability with good results. © 2013 Springer-Verlag.