| 基于PSOGA的LS-SVM模型在时间序列预测中的应用 |
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| 引用本文： | 王斌斌,姚 远,张晓丽.基于PSOGA的LS-SVM模型在时间序列预测中的应用[J].信阳师范学院学报(自然科学版),2014(2):271-274. |
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| 作者姓名： | 王斌斌 姚 远 张晓丽 |
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| 作者单位： | 河南城建学院计算机科学与工程系;信阳师范学院物理电子工程学院; |
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| 基金项目： | 国家自然科学基金项目(51275239);河南省科学技术研究重点项目(13A510770);信阳师范学院青年骨干教师资助计划 |
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| 摘 要： | 使用PSO与GA结合的混合算法PSOGA对最小二乘支持向量机(LS-SVM)模型的参数进行了优化,搜索到更优的参数,提高了模型的时间序列预测精度.在Mackey-Glass、Lorenz时间序列上的实验结果表明:本文模型预测精度较高.
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| 关 键 词： | PSO算法 GA算法 LS-SVM 时间序列预测 |
Application of LS-SVM Based PSOGA Model to Time Series Prediction |
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| Institution: | ,Department of Computer Science and Engineering,Henan University of Urban Construction,College of Physics and Electronic Engineering,Xinyang Normal University |
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| Abstract: | The LS-SVM model parameters were optimized by using PSOGA hybrid algorithm which combined PSO with GA,the better parameters were searched and the time series prediction accuracy of the model were improved. The experimental results on Mackey-Glass、Lorenz time series further verified the effectiveness of our model. |
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| Keywords: | PSO algorithm GA algorithm LS-SVM time series prediction |
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