Technical data mining with evolutionary radial basis function classifiers
Applied Soft Computing
Genetically evolved radial basis function network based prediction of drill flank wear
Engineering Applications of Artificial Intelligence
Design of fuzzy radial basis function-based polynomial neural networks
Fuzzy Sets and Systems
Reformulating Learning Vector Quantization and Radial Basis Neural Networks
Fundamenta Informaticae
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An evolutionary neural network training algorithm is proposed for radial basis function (RBF) networks. The locations of basis function centers are not directly encoded in a genetic string, but are governed by space-filling curves whose parameters evolve genetically. This encoding causes each group of codetermined basis functions to evolve to fit a region of the input space. A network produced from this encoding is evaluated by training its output connections only. Networks produced by this evolutionary algorithm appear to have better generalization performance on the Mackey-Glass time series than corresponding networks whose centers are determined by k-means clustering