首页 | 本学科首页   官方微博 | 高级检索  
     

基于模拟退火算法支持向量机在枯水期月径流预测中的应用
引用本文:魏,胜.基于模拟退火算法支持向量机在枯水期月径流预测中的应用[J].水资源与水工程学报,2015,26(2):135-138.
作者姓名:  
作者单位:云南省水文水资源局文山分局
摘    要:鉴于支持向量机(SVM)最佳算法参数难以确定的不足,利用模拟退火算法(SA)搜索SVM学习参数,提出SA-SVM预测模型,并与基于遗传算法(GA)搜索SVM学习参数的GA-SVM模型作对比,以云南省龙潭站枯水期1-3月月径流预测为例进行实例研究,利用实例前43年和后10年资料对模型进行训练和预测。结果表明:SA-SVM模型对实例后10年枯水期1-3月月均径流预测的平均相对误差绝对值分别为3.11%、4.93%和6.75%,精度优于GA-SVM模型,表明SA-SVM模型具有较高的预测精度和泛化能力。SA算法通过赋予搜索过程一种时变且最终趋于零的概率突跳性,有效避免了算法陷入局部极值并最终趋于全局最优。

关 键 词:径流预测  模拟退火算法  支持向量机  参数优化  枯水期

Application of support vector machine to prediction monthly runoff in dry season based on simulation annealing algorithm
WEI Sheng.Application of support vector machine to prediction monthly runoff in dry season based on simulation annealing algorithm[J].Journal of water resources and water engineering,2015,26(2):135-138.
Authors:WEI Sheng
Affiliation:WEI Sheng;Wenshan Branch Bureau,Yunnan Province Hydrology Water Resources Bureau;
Abstract:Aimed at the shortage that the best algorithm parameters of support vector machine (SVM) deficiency is difficult to determine, the paper used the simulation annealing algorithm (SA) to search SVM learning parameters and put forward SA-SVM prediction model.Based on comparison between genetic algorithm (GA) and GA-SVM model in searching SVM learning parameters,it took the runoff prediction in dry season of January to march at LongTan station in Yunnan Province as an example.It trained and forecasted the model by using data of the example before 43 years and after 10 years. The results show that the absolute values of average relative errors of prediction runoff in 3 months of dry season by SA-SVM model after 10 years of instance were 3.11%, 4.93% and 6.75%, the accuracy is better than that by GA-SVM model, which showed that SA-SVM model has higher prediction accuracy and generalization ability. The sudden jump time-varying and final trend to zero probability by SA algorithm through giving the search process can effectively avoid the algorithm falls into local extreme and tends to the global optimal.
Keywords:
本文献已被 CNKI 等数据库收录!
点击此处可从《水资源与水工程学报》浏览原始摘要信息
点击此处可从《水资源与水工程学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号