SELF-GENERATION FUZZY MODELING SYSTEMS THROUGH HIERARCHICAL RECURSIVE-BASED PARTICLE SWARM OPTIMIZATION

Abstract

ABSTRACT In this article, fuzzy modeling systems are automatically developed by Hierarchical Recursive-Based Particle Swarm Optimization (HRPSO). HRPSO, which contains Fuzzy C-Mean (FCM) clustering, Particle Swarm Optimization (PSO), and recursive least-squares technology, self constructs adjustable parameters for approximating a nonlinear function and a discrete dynamic system. In general, the heuristic PSO is an evolutional computing technology when solving complex and global problems. However, the necessary training time is unsuitable for large population sizes and many adjustable parameters. To quickly approximate the actual output of nonlinear functions, the input-output training data is initially clustered by an FCM algorithm. From there favorable features are extracted from the training data and some fuzzy structures with fewer adjustable parameters will be collected as the initial population of the PSO. The FCM procedure is used to directly extract necessary small populations of PSO from training samples. After that, the recursive-based PSO is proposed to tune some adjustable parameters to quickly construct the desired fuzzy modeling system. Therefore, the proposed HRPSO determines fuzzy modeling systems with a small number of fuzzy rules to approach high accuracy within a short training time. Simulation results demonstrate the efficiency of our fuzzy model systems to solve two nonlinear problems.