摘要
将蚁群算法与径向基(RBF)网络相结合,提出一种用于堆石坝力学参数反演的蚁群聚类径向基网络模型。该模型用蚁群聚类算法搜索RBF网络基函数中心,模拟蚁群觅食聚类的概率转移特性,所得到的聚类结果类间离散度和比传统K均值聚类结果小,能够得到更合理的基函数中心,从而获得较准确的坝体参数和位移之间的非线性映射关系。在进行参数灵敏度分析的基础,对一座堆石坝的反演分析表明,蚁群聚类RBF网络模型可有效地求解堆石坝多参数反演问题,反演结果优于BP网络模型和K均值RBF网络模型。
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.
出处
《岩石力学与工程学报》
EI
CAS
CSCD
北大核心
2009年第A02期3639-3644,共6页
Chinese Journal of Rock Mechanics and Engineering
基金
教育部创新团队资助项目(IRT0518)
关键词
岩土工程
土石坝
蚁群聚类算法
RBF网络
灵敏度
反演分析
geotechnical engineering
earth-rockfill dam
ant colony clustering
radial basis function（RBF） network
sensitivity
inverse analysis
