An Optimization of Granular Networks Based on PSO and Two-Sided Gaussian Contexts

This paper is concerned with an optimization of GN (Granular Networks) based on PSO (Particle Swarm Optimization) and Information granulation). The GN is designed by the linguistic model using context-based fuzzy c-means clustering algorithm performing relationship between fuzzy sets defined in the input and output space. The contexts used in this paper are based on two-sided Gaussian membership functions. The main goal of optimization based on PSO is to find the number of clusters obtained in each context and weighting factor. Finally, we apply to coagulant dosing process in a water purification plant to evaluate the predication performance and compare the proposed approach with other previous methods. Keywords-granular networks; particle swarm optimization; linguistic model; two-sided Gaussian contexts. I. INTRODUCTION

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