Genetic-Based Granular Radial Basis Function Neural Network

Abstract

In this paper, we introduce a new architecture of GA-based Granular Radial Basis Function Neural Networks (GRBFNN) and discuss its comprehensive design methodology. The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-means which takes into account the structure being already formed in the output space. The GA-based design procedure being applied to each receptive fields of GRBFNN leads to the selection of preferred receptive fields with specific local characteristics (such as the number of context, the number of clusters for each context, and the input variables for each context) available within the GRBFNN.