文档介绍：基于粒子群最小二乘支持向量机的水文预测
摘要:支持向量机理论为研究屮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. 1 ssvm) o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 南械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。
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 model based on particle swarm optimization for hydrological forecasting
li 1,2 *, li yu. xia 1
school of water resources and hydr o. electric eng in eer ing, xi' an university of technology, xi， an shaanxi
710048,china ;
school of computer science, shaanxi normal university,
xi' an shaanxi 710062,china
abstract:
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・ avoiding waste of time and unsatisfactory in conventional parameter choosing method, a 1 east square support vector machine (Is・ svm) model based on particle swarm optimizer (pso) is given in this paper, the model is built by using the regression principle of least square support vector machine , the key parameters in this model are 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・
support vector machine (svm) algorithm provides a new way for the study of mid・ and.