| 引用本文: | 张冬青,宁宣熙,刘雪妮.基于RBF神经网络的非线性时间序列在线预测[J].控制理论与应用,2009,26(2):151~155.[点击复制] |
| ZHANG Dong-qing,NING Xuan-xi,LIU Xue-ni.On-line prediction of nonlinear time series using RBF neural networks[J].Control Theory and Technology,2009,26(2):151~155.[点击复制] |
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| 基于RBF神经网络的非线性时间序列在线预测 |
| On-line prediction of nonlinear time series using RBF neural networks |
| 摘要点击 2682 全文点击 2265 投稿时间：2007-08-15 修订日期：2008-07-11 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.2.007 |
| 2009,26(2):151-155 |
| 中文关键词 预测 径向基函数神经网络 隐马尔可夫模型 序列蒙特卡罗方法 |
| 英文关键词 prediction radial basis function neural networks hidden Markov model sequential Monte Carlo method |
| 基金项目 国家自然科学基金资助项目(70571037); 国家软科学研究计划资助项目(2006GXQ3B203). |
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| 中文摘要 |
| 针对非线性非高斯时间序列, 提出观测噪声服从隐马尔可夫模型(HMM)的径向基函数(RBF)神经网络(RBF-HMM)预测模型, 其特点在于模型输入包含误差反馈项、RBF网络隐含层节点数的可变性和观测噪声的隐马尔可夫性; 并采用序列蒙特卡罗(SMC)方法实现基于RBF-HMM模型的时间序列在线预测. 最后采用太阳黑子数平滑月均值数据和CRU国际钢材价格指数月数据进行实证研究, 结果表明该模型的有效性. |
| 英文摘要 |
| For the nonlinear and non-Gaussian time series, a novel predictive model—the RBF-HMMmodel is proposed based on the radial basis function(RBF) neural network with measurement noise being assumed to be of a hidden Markov model(HMM). The characteristics of this model include: 1) the predictive errors of RBF neural network are associated with the input of RBF-HMM model; 2) the number of hidden neurons varies with time; 3) the measurement noise is assumed to be HMMdistributed. SequentialMonte Carlo(SMC) method is then applied to the on-line prediction for time series in RBFHMMmodel.
Finally, the smoothed data of the monthly mean sunspot numbers and CRU(the Britain Commodity Research University) steel price index are analyzed. The experimental results indicate that the RBF-HMM model is effective. |