Hierarchical radial basis function neural networks for classification problems
- Chen, Yuehui; Peng, Lizhi; Abraham, Ajith
- Issue Date
- SPRINGER-VERLAG BERLIN
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1, v.3971, pp.873 - 879
- Journal Title
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1
- Start Page
- End Page
- The 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.
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- College of Software > School of Computer Science and Engineering > 1. Journal Articles
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