Design of Radial Basis Function Network Using Adaptive Particle Swarm Optimization and Orthogonal Least Squares

Majid Moradi Zirkohi, Mohammad Mehdi Fateh, Ali Akbarzade
DOI: 10.4236/jsea.2010.37080                

Abstract

This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.

Keywords

Radial Basis Function Network, Orthogonal Least Squares Algorithm, Particle Swarm Optimization, Mackey-Glass Chaotic Time-Series

Conflicts of Interest

The authors declare no conflicts of interest.

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