| 混沌时间序列的LSSVM预测方法 |
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| 引用本文： | 莫小琴,李钟慎.混沌时间序列的LSSVM预测方法[J].华侨大学学报(自然科学版),2014,0(4):373-377. |
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| 作者姓名： | 莫小琴 李钟慎 |
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| 作者单位： | 华侨大学 机电及自动化学院, 福建 厦门 361021 |
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| 基金项目： | 福建省自然科学基金资助项目(2011J01350);中央高校基本科研业务费资助项目(JB-ZR1107) |
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| 摘 要： | 结合相空间重构理论,提出运用最小二乘支持向量机(LSSVM)建立混沌时间序列的预测模型,并用粒子群优化(PSO)解决LSSVM参数寻优的问题.通过与RBF神经网络构建的预测模型相比较,计算预测模型的均方根误差来评价模型的性能.结果表明:采用PSO优化的LSSVM预测模型的预测精度更高.
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| 关 键 词： | 混沌时间序列 相空间重构 最小二乘支持向量机 粒子群优化 预测模型 |
Prediction Method of the Chaotic Time Series Using Least Squares Support Vector Machine |
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| MO Xiao-qin;LI Zhong-shen.Prediction Method of the Chaotic Time Series Using Least Squares Support Vector Machine[J].Journal of Huaqiao University(Natural Science),2014,0(4):373-377. |
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| Authors: | MO Xiao-qin;LI Zhong-shen |
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| Institution: | College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China |
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| Abstract: | Based on the phase space reconstruction theory, prediction model of chaotic time series using least squares support vector machine(LSSVM)was presented in this paper, and particle swarm optimization(PSO)was used to solve the LSSVM parameter optimization problems. By comparing with the established prediction model of RBF neural network, the root mean square error of the prediction model was calculated to evaluate the performance of the model. The results show that the PSO optimized LSSVM prediction model has higher prediction accuracy. |
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| Keywords: | chaotic time series phase space reconstruction least squares support vector machine particle swarm optimization prediction model |
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