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基于小波变换与差分变异BSO-BP算法的大坝变形预测
引用本文:陈俊风,王玉浩,张学武,薛醒思. 基于小波变换与差分变异BSO-BP算法的大坝变形预测[J]. 控制与决策, 2021, 36(7): 1611-1618
作者姓名:陈俊风  王玉浩  张学武  薛醒思
作者单位:河海大学物联网工程学院,江苏常州213022;福建工程学院福建省汽车电子与电驱动重点实验室,福州350118
基金项目:国家重点研发计划项目(2018YFC0407101);中央高校基本科研业务费专项资金项目(2019B22314).
摘    要:针对现有大坝变形预测模型的预测精度不高、BP神经网络的参数和结构很难确定且容易陷入局部极值等问题,通过引入小波变换理论把原始的大坝变形序列分解成多个子序列,然后对每个子序列使用头脑风暴优化算法(brain storm optimization,BSO)优化BP神经网络的参数和结构.同时,把差分变异思想引入BSO算法,建...

关 键 词:大坝变形  预测  小波变换  BP神经网络  差分变异  头脑风暴优化算法

Dam deformation prediction based on wavelet transform and differential mutation BSO-BP algorithm
CHEN Jun-feng,WANG Yu-hao,ZHANG Xue-wu,XUE Xing-si. Dam deformation prediction based on wavelet transform and differential mutation BSO-BP algorithm[J]. Control and Decision, 2021, 36(7): 1611-1618
Authors:CHEN Jun-feng  WANG Yu-hao  ZHANG Xue-wu  XUE Xing-si
Affiliation:College of IoT Engineering,Hohai University,Changzhou 213022,China; Fujian Key Lab for Automotive Electronics and Electric Drive,Fujian University of Technology,Fuzhou 350118,China
Abstract:The existing dam deformation prediction model has low accuracy. It is difficult to determine the parameters and structure of the BP neural network and is easy to fall into the local extremum. Therefore, this paper introduces the wavelet transform theory to decompose the original dam deformation sequence into several subsequences, then uses the brain storm optimization(BSO) algorithm to optimize the parameters and structure of the BP neural network for each subsequence. At the same time, this paper applies the differential mutation idea to the basic BSO algorithm and establishes a dam deformation prediction model based on the wavelet transform and differential mutation BSO algorithm for optimizing the BP neural network. The results of the present study indicate that the proposed prediction model has higher prediction accuracy than other prediction models.
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