A differential evolution based incremental training method for RBF networks

Authors:
Junhong Liu;Jouni Lampinen
Affiliations:
Lappeenranta University of Technology, Lappeenranta, Finland;Lappeenranta University of Technology, Lappeenranta, Finland
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

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Abstract

The Differential Evolution (DE) is a floating-point encoded evolutionary strategy for global optimization. It has been demonstrated to be an efficient, effective, and robust optimization method, especially for problems containing continuous variables. This paper concerns applying a DE-based algorithm to training Radial Basis Function (RBF) networks with variables including centres, weights, and widths of RBFs. The proposed algorithm consists of three steps: the first step is the initial tuning, which focuses on searching for the center, weight, and width of a one-node RBF network, the second step is the local tuning, which optimizes the three variables of the one-node RBF network --- its centre, weight, and width, and the third step is the global tuning, which optimizes all the parameters of the whole network together. The second step and the third step both use the cycling scheme to find the parameters of RBF network. The Mean Square Error from the desired to actual outputs is applied as the objective function to be minimized. Training the networks is demonstrated by approximating a set of functions, using different strategies of DE. A comparison of the net performances with several approaches reported in the literature is given and shows the resulting network performs better in the tested functions. The results show that proposed method improves the compared approximation results.