A Two-Phase Evolutionary Method to Train RBF Networks
Source abstract
Abstract
This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic algorithm is incorporated to locate the best RBF neural network for the underlying problem. The method is compared against other training methods of RBF neural networks on a wide series of classification and regression problems from the relevant literature and the results are reported.
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Research context
OpenAlex topics: Neural Networks and Applications · Fuzzy Logic and Control Systems · Advanced Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece
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