Neuron Selection for RBF Neural Network Classifier Based on Multiple Granularities Immune Network

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Abstract

The central problem in training a radial basis function neural network is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose to select hidden layer neurons based on multiple granularities immune network. Firstly a multiple granularities immune network (MGIN) algorithm is employed to reduce the data and get the candidate hidden neurons and construct an original RBF network including all candidate neurons. Secondly, the removing redundant neurons procedure is used to get a smaller network. Some experimental results show that the network obtained tends to generalize well.