Abstract
The idea of using RBF neural networks for fuzzy rule extraction from numerical data is not new. The structure of this kind of architectures, which supports clustering of data samples, is favorable for considering clusters as if-then rules. However, in order for real if-then rules to be derived, proper antecedent parts for each cluster need to be constructed by selecting the appropriate subspace of input space that best matches each cluster’s properties. In this paper we address the problem of antecedent part construction by (a) initializing the hidden layer of an RBF-Resource Allocating Network using an unsupervised clustering technique whose metric is based on input dimensions that best relate the data samples in a cluster, and (b) by pruning input connections to hidden nodes in a per node basis, using an innovative Genetic Algorithm optimization scheme.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Wallace, M., Tsapatsoulis, N. (2005). Combining GAs and RBF Neural Networks for Fuzzy Rule Extraction from Numerical Data. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_82
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DOI: https://doi.org/10.1007/11550907_82
Publisher Name: Springer, Berlin, Heidelberg
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