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稀疏贝叶斯及其在时间序列预测中的应用
Sparse Bayesian and Its Application to Time Series Forecasting
【摘要】 阐述了稀疏贝叶斯方法在时间序列预测中应用的理论基础,将稀疏贝叶斯方法应用于Log istic方程产生的混沌时间序列和发动机油滑数据的预测,并与支持向量机(SVM)和RBF神经网络时间序列预测进行了比较.实验结果表明,稀疏贝叶斯方法不仅具有SVM的性能,而且比SVM使用更少的核函数,取得了较好的预测效果.
【Abstract】 The basic theoretic analysis of sparse Bayesian method in time series forecasting is introduced.Chaotic time series produced by Logistic equation and some type of engine lubrication time series are used for feasibility validation.In order to show its superiority,support vector machine(SVM) and RBF neural networks forecaster are also used during numerical simulations.Examples show that sparse Bayesian classification achieves comparable recognition accuracy to the SVM,and also requires substantially fewer kernel functions.Experimental results show the better performance in forecasting.
【Key words】 Sparse Bayesian classification; Support vector machine; Nonlinear forecasting; RBF neural network;
- 【文献出处】 控制与决策 ,Control and Decision , 编辑部邮箱 ,2006年05期
- 【分类号】TP183
- 【被引频次】24
- 【下载频次】793