Hybrid learning of RBF networks

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Abstract

Three different learning methods for RBF networks and their combinations are presented. Standard gradient learning, three-step algoritm with unsupervised part, and evolutionary algorithm are introduced. Their perfromance is compared on two benchmark problems: Two spirals and Iris plants. The results show that three-step learning is usually the fastest, while gradient learning achieves better precission. The combination of these two approaches gives best results. © Springer-Verlag 2002.

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APA

Neruda, R., & Kudová, P. (2002). Hybrid learning of RBF networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2331 LNCS, pp. 594–603). Springer Verlag. https://doi.org/10.1007/3-540-47789-6_62

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