摘要
支持向量机理论为研究中长期水文预测提供了新的方法。针对最小二乘支持向量机模型参数选择费时且效果差这一问题,给出基于粒子群算法的最小二乘支持向量机水文预测模型(PSO-LSSVM)。该模型运用最小二乘支持向量机回归原理建立,参数选取采用具有全局搜索能力的粒子群算法进行寻优。用此模型对南桠河冶勒水电站月径流进行预测,仿真计算结果表明,该算法可提高预测效率与预测精度。
Support Vector Machine（SVM） algorithm provides a new way for the study of mid-and-long term hydrological forecasting that needs a learning of finite samples.Concerning the time-consumption and unsatisfactory performance in the conventional parameter choosing method,a Least Square Support Vector Machine（LS-SVM） model based on Particle Swarm Optimization（PSO） was given in this paper.The model was built by using the regression principle of least square support vector machine,the key parameters in this model were optimized by PSO algorithm with random seeking strategy.Monthly runoff forecasting in Yele Hydropower Station on Nanya river indicates that the algorithm is able to promote efficiency and accuracy.
出处
《计算机应用》
CSCD
北大核心
2012年第4期1188-1190,共3页
journal of Computer Applications
基金
国家火炬计划基金资助项目(07C26213711606)
陕西省自然科学基础研究计划项目(SJ08E220)
关键词
最小二乘支持向量机
粒子群算法
水文预测
参数优化
回归
Least Square-Support Vector Machines（LS-SVM）
Particle Swarm Optimization（PSO）
hydrological forecasting
parameter optimization
regression
