Title: Study of optimized RBF network in feature selection
Abstract: An adaptive quantum-behaved particle swarm optimization(AQPSO)algorithm is firstly proposed in order to improve the performance of RBF network.By applying AQPSO algorithm to train the central position and width of the basis function adopted in the RBF network,and computing the weights of the network with least-square method,the generalization ability of the RBF neural network is improved,and then applied to select features.Experimental results show that obtained network model not only has good generalization properties,but also has better stability.It illustrates that RBF neural network with AQPSO algorithm can acquire the feature subset which is more representative.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
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Cited By Count: 2
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