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- W1582119449 abstract "AbstractThe purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network can be created and evolved by using tree-structure based evolutionary algorithm. This framework allows input variables selection, over-layer connections for the various nodes involved. The HRBF structure is developed using an evolutionary algorithm and the parameters are optimized by particle swarm optimization algorithm. Empirical results on benchmark classification problems indicate that the proposed method is efficient.KeywordsParticle Swarm OptimizationParticle Swarm Optimization AlgorithmRadial Basis Function Neural NetworkRadial Basis Function NetworkGaussian Radial Basis FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1582119449 date "2006-01-01" @default.
- W1582119449 modified "2023-09-23" @default.
- W1582119449 title "Hierarchical Radial Basis Function Neural Networks for Classification Problems" @default.
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- W1582119449 doi "https://doi.org/10.1007/11759966_128" @default.
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