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《Yangtze River》 2013-15
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Application of improved RBF neural network method in dam deformation analysis

WU Jinkun1,LIU Jingran1,XUE Shunhua2,ZHANG Jiaojiao3(1.College of Water Conservancy and Hydropower Engineering,Hebei University of Engineering,Handan 056038,China;2.Qinghai Yuxing Water Conservancy and Hydropower Design Co.,Ltd,Xining 810001,China;3.Hebei Research Institute of Investigation and Design of Water Conservancy and Hydropower,Tianjin 300250,China)  
For the faults of local optimal solution appearance and slow convergence existed in RBF neural network,an inertial weight is introduced to improve the Shuffled Frog Leaping Algorithm(SFLA),and then the RBF neural network is optimized by the improved SFLA.By setting a rational initial weight,the improved SFLA can revise the renewal strategy of frog group,leap out the local optimal solution and avoid early maturity,and possesses an ability to balance local search and global search,thus solving the problems of local optimal solution and slow convergence.When the improved RBF neural network is applied in a dam deformation analysis,the prediction accuracy of the model is improved greatly,which is in good accordance with the measured data,showing its good engineering application value.
【Fund】: 河北省自然科学基金项目(E2012402049);; 中国水利水电科学研究院流域水循环模拟与调控国家重点实验室项目(IWHR-SKL-201216)
【CateGory Index】: TV698.1
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