Abstract：An ant colony clustering radial basis function neural network model for parameter inverse analysis is proposed by combining the ant colony clustering algorithm with radial basis function(RBF) networks. In the new model, the radial basis function centers are searched by the ant colony clustering algorithm which utilizes the probability transfer characteristic of ant foraging clustering behavior. The sum of scatter degree obtained by the ant colony clustering algorithm is smaller than that obtained by the traditional K means clustering algorithm, thus more reasonable radial basis function centers can be searched so as to obtain the nonlinear mapping relationship between the parameters to be inversed and the displacements measured at certain points in the dam. Inverse analysis is performed to a concrete faced rockfill dam；the results show that the new neural network model can solve the inverse analysis problem of rockfill dams efficiently，which outperforms BP neural network model and K means RBF neural network model.
康飞，李俊杰，许青. 堆石坝参数反演的蚁群聚类RBF网络模型[J]. 岩石力学与工程学报, 2009, 28(S2): 3639-3644.
KANG Fei，LI Junjie，XU Qing. ANT COLONY CLUSTERING RADIAL BASIS FUNCTION NETWORK MODEL FOR INVERSE ANALYSIS OF ROCKFILL DAM. , 2009, 28(S2): 3639-3644.
