Abstract
We extend our previous theoretical results concerning functional equivalence of Gaussian RBF networks and test the proposed canonical genetic learning algorithm on two problems. In our experiments, canonical learning achieved the same error threshold about two times faster in comparison to standard GA.
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© 1998 Springer-Verlag Wien
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Neruda, R. (1998). Canonical Genetic Learning of RBF Networks Is Faster. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_77
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_77
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive