Abstract
The effects of a neural network’s topology on its performance are well known, yet the question of finding optimal configurations automatically remains largely open. This paper proposes a solution to this problem for RBF networks. A self-optimising approach, driven by an evolutionary strategy, is taken. The algorithm uses output information and a computationally efficient approximation of RBF networks to optimise the K-means clustering process by co-evolving the two determinant parameters of the network’s layout: the number of centroids and the centroids’ positions. Empirical results demonstrate promise.
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Burdsall, B., Giraud-Carrier, C. (1998). GA-RBF: A Self-Optimising RBF Network. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_76
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_76
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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