Fast forward RBF network construction based on particle swarm optimization

Authors:
Jing Deng;Kang Li;George W. Irwin;Minrui Fei
Affiliations:
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Year:
2010

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Abstract

The conventional forward RBF network construction methods, such as Orthogonal Least Squares (OLS) and the Fast Recursive Algorithm (FRA), can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trail-and-error, or generated randomly. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. This paper investigates a new forward construction algorithm for RBF networks. It utilizes the Particle Swarm Optimization (PSO) method to search for the optimal RBF centres and their associated widths. The efficiency of this network construction procedure is retained within the forward construction scheme. Numerical analysis shows that the FRA with PSO included only needs about two thirds of the computation involved in a PSO assisted OLS algorithm. The effectiveness of the proposed technique is confirmed by a numerical simulation example.