基于SSPSO优化GRNN的水电站厂房结构振动响应预测

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (4) : 104-109.

论文

基于SSPSO优化GRNN的水电站厂房结构振动响应预测

徐国宾1，韩文文1，2,王海军1

作者信息 +

Vibration response prediction of powerhouse structurebased on SSPSO-GRNN

XU Guo-bin1，HAN Wen-wen1，2,WANG Hai-jun1

Author information +

文章历史 +

摘要

提出基于优胜劣汰、步步选择的粒子群优化算法（缩写为SSPSO），弥补了一般粒子群优化算法容易陷入局部极值、早熟收敛或停滞的缺陷。并运用SSPSO对广义回归神经网络（GRNN）平滑参数P进行优化，充分利用SSPSO寻优能力强及径向基函数调整参数少的优点，建立厂房结构的振动响应预测模型，对某厂顶溢流式水电站的厂坝结构振动响应问题展开预测研究。通过分析预测效果得出：与一般的粒子群算法相比，本文提出的SSPSO算法的寻优能力得到了很大的提高。与此同时，基于SSPSO优化的广义回归神经网络（SSPSO-GRNN）与其他网络相比，在预测精度、收敛性能、泛化能力等各个方面得到了很大提升。为水电站厂房振动响应预测提供了新的方法和思路，为增强厂房结构的智能化监测提供了保障。

Abstract

Particle swarm optimization algorithm (PSO) is easy to fall into local extremum and premature convergence. To make up the defect of PSO，put forward a new kind of PSO which based on survival of the fittest and step by step selection(referred to as SSPSO). Then use SSPSO to optimize smoothness value of generalized regression neural network(GRNN). It makes full use the dvantages of optimization ability of SSPSO and fewer parameters of GRNN. Then set up vibration response prediction model which based on research data of crest overflow hydropower station. Predicted results show that search capability of SSPSO has been greatly improved compared with PSO. At the same time，prediction accuracy、convergence performance and generalization ability of SSPSO-GRNN is better than others. Puts forward a new method for vibration response prediction of hydropower stations，and provides guarantee for enhancing intelligent monitoring.

导出引用

徐国宾;韩文文;王海军. 基于SSPSO优化GRNN的水电站厂房结构振动响应预测[J]. 振动与冲击, 2015, 34(4): 104-109

XU Guo-bin;HAN Wen-wen;WANG Hai-jun. Vibration response prediction of powerhouse structurebased on SSPSO-GRNN[J]. Journal of Vibration and Shock, 2015, 34(4): 104-109