Training RBF Networks Using a DE Algorithm with Adaptive Control

Authors:
Junhong Liu;Jorma Mattila;Jouni Lampinen
Affiliations:
Lappeenranta University of Technology;Lappeenranta University of Technology;Lappeenranta University of Technology
Venue:
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Year:
2005

Citing 0
Cited 2

A Novel Radial Basis Function Networks Locally Tuned with Differential Evolution for Classification: An Application in Medical Science

International Journal of Systems Biology and Biomedical Technologies
DE+RBFNs based classification: A special attention to removal of inconsistency and irrelevant features

Engineering Applications of Artificial Intelligence

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Abstract

This paper concerns the application of differential evolution to training radial basis function networks. The algorithm consists of initial tuning, local tuning, and global tuning. The last two tunings both use a cycle-increased searching scheme, and global tuning employs fuzzy adaptive control. The mean square error from desired to actual outputs is applied as the objective function. Four standard test functions is used for demonstration. A comparison of net performances with two approaches reported in the literature shows the resulting network performs better in terms of a lower mean square error with a smaller network.