利用PSO-SA混合优化支持向量回归的径流预报模型研究  

Runoff forecasting based on hybrid optimized SVR using PSO-SA

作　　者：蒋林利 李洁 吴建生 Jiang Linli;Li Jie;Wu Jiansheng(Dept. of Mathematics & Computer Science,Guangxi Science & Technology Teachers College,Laibin Guangxi 546199,China;School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]广西科技师范学院数学与计算机科学学院,广西来宾546199 [2]武汉理工大学信息工程学院,武汉430070

出　　处：《计算机应用研究》2019年第9期2599-2603,共5页Application Research of Computers

基　　金：国家科技部中小企业创新基金项目(13C26214504766);广西科技厅面上基金资助项目(2014GXNSFAA118027);2017年广西高校中青年教师基础能力提升项目(2017KY0896);广西教育厅高校科研项目(YB2014467)

摘　　要：为了有效提高径流预报的准确度,提出一种有效的融合优化策略,采用基于粒子群和模拟退火算法相结合的混合方法同时优化支持向量回归核函数类型和内核参数,以此建立一种有效的混合优化支持向量回归径流预报模型,为核函数选择和参数优化提供了一种有效途径。通过对广西柳州柳江径流实例分析,并与纯粹的支持向量回归模型对比,研究结果表明,该模型预测稳定,具有较高泛化性能和预测准确度,为径流预报提供了一种有效预测方法。In order to effectively improve the accuracy of runoff forecasting, this paper proposed an effective hybrid optimization strategy which was based on the combination of particle swarm optimization and simulated annealing algorithm, and it also optimized the type of kernel function and the kernel parameter setting of support vector regression to establish an effective hybrid optimization support vector regression runoff forecasting model. The proposed method provided an effective way for the choice of kernel functions and parameter optimization. By analyzing the examples of Guangxi Liujiang’s River runoff and with pure support vector regression model comparison, the results of the study show that the model is stable in prediction and it has high generalization performance and accuracy of prediction, and it can provide an effective prediction method for runoff forecast.

关 键 词：支持向量回归 粒子群 模拟退火 融合改进 径流预报模型 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]