支持向量机时间序列预测模型的参数影响分析与自适应优化

杨虞微; 左洪福; 陈果

支持向量机时间序列预测模型的参数影响分析与自适应优化

南京航空航天大学民航学院, 南京 210016

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出版历程
- 收稿日期: 2005-07-30
- 修回日期: 2005-12-22
- 刊出日期: 2006-08-28

Influence analysis and self-adaptive optimization of support vector machine time series forecasting model parameters

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

摘要

摘要: 建立在统计学习理论和结构风险最小原则上的支持向量机在理论上保证了模型的最大泛化能力,因此与建立在经验风险最小原则上的神经网络模型相比,理论上更为完善。本文运用支持向量机建立时间序列预测模型,研究影响模型预测精度的相关参数,在分析参数对时间序列预测精度的影响基础上,提出用遗传算法建立支持向量机预测模型的参数自适应优化算法。最后,用太阳黑子数据和航空发动机油样光谱数据进行了预测分析。算例表明了本文算法的正确性。
- 航空、航天推进系统 /
- 支持向量机 /
- 时间序列分析 /
- 预测 /
- 遗传算法 /
- 优化
Abstract: Support Vector Machine(SVM) is based on Statistical Learning Theory(SLT) and Structural Risk Minimization Principle(SRM),and theoretically assures best generalization,therefore,it is theoretically better than Artificial Neural Network(ANN) which is based on Empirical Risk Minimization Principle(ERM).In this paper,SVM was used to establish time series forecasting model,and on the basis of analyzing the influence of model parameters,a self-adaptive optimizing algorithm based on genetic algorithm was put forward.Finally,the sunspot data and the spectrometric oil data of some aero-engines were used for preliminary analysis,and the results show the correctness and validity of the new method.
- Aerospace propulsion system /
- Support Vector Machine(SVM) /
- Time series analysis /
- Forecasting /
- Genetic algorithm /
- Optimization

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参考文献(9)

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