基于小波变换与差分变异BSO-BP算法的大坝变形预测

Dam deformation prediction based on wavelet transform and differential mutation BSO-BP algorithm

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,建立一种基于小波变换和差分变异头脑风暴算法优化BP神经网络的大坝变形预测模型.实验结果表明,与其他预测模型相比,所提出的预测模型具有更高的预测精度.

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.