基于独立模型的非线性时间序列多步超前预测

上海交通大学学报（自然版） ›› 2013, Vol. 47 ›› Issue (10): 1626-1631.

• 自动化技术、计算机技术 • 上一篇下一篇

基于独立模型的非线性时间序列多步超前预测

杨臻明1,岳继光1,王晓保2,萧蕴诗1

(1．同济大学电子与信息工程学院, 上海, 201804; 2．上海申通轨道交通研究咨询有限公司, 上海, 201103)

收稿日期:2012-12-14 出版日期:2013-10-30 发布日期:2013-10-30
基金资助:
国家自然科学基金(40872090)资助项目

Multistep-Ahead Independent Prediction of Nonlinear Time Series Based on Independent Model

YANG Zhenming1,YUE Jiguang1,WANG Xiaobao2,XIAO Yunshi1

(1. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China;2. Shanghai Shentong Rail Transit Research and Consultancy Co., Ltd., Shanghai 201103, China)

Received:2012-12-14 Online:2013-10-30 Published:2013-10-30

摘要/Abstract

摘要：

提出一种非线性时间序列的多步超前独立预测方法. 对比逐步递归方法和独立预测方法, 分析了积累误差对多步超前预测性能的影响. 采用递归神经网络(RNN)实现了独立预测方法, 建立了城市轨道交通能耗预测模型. 通过MATLAB训练和测试该模型, 比较了两种方法下的多步超前预测输出. 结果表明，独立预测方法的误差优于逐步递归方法. 最后指出了独立预测方法的优缺点及适用范围.

关键词: 非线性时间序列, 多步超前独立预测, 积累误差, 递归神经网络

Abstract:

A multistep-ahead independent prediction approach of nonlinear time series was proposed. The step-by-step recurrent approach and the independent approach were compared, and the influence of accumulative error on the performance of multistep-ahead prediction was analyzed. The recurrent neural network (RNN) was used to realize the independent prediction approach, and the predictive model of urban rail transit is built, trained and tested by MATLAB. The predictive results showed that the error of independent prediction was smaller than that of step-by-step recurrent approach. The advantages and disadvantages of each approach were analyzed.

Key words：

Key words: nonlinear time series, multistep-ahead independent prediction, accumulative error, recurrent neural network

中图分类号:

TP 273

杨臻明1,岳继光1,王晓保2,萧蕴诗1. 基于独立模型的非线性时间序列多步超前预测[J]. 上海交通大学学报（自然版）, 2013, 47(10): 1626-1631.

YANG Zhenming1,YUE Jiguang1,WANG Xiaobao2,XIAO Yunshi1. Multistep-Ahead Independent Prediction of Nonlinear Time Series Based on Independent Model[J]. Journal of Shanghai Jiaotong University, 2013, 47(10): 1626-1631.

参考文献

［1］Badjate S L, Dudul S V. Multi step ahead prediction of North and South hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model［J］. WSEAS Transactions on Information Science and Applications, 2009, 6(4): 684693.

［2］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.

Zhang D Q, Ning X X, Liu X N. Onling prediction of nonlinear time series using RBF neural networks［J］. Control Theory & Applications, 2009, 26(2): 151155.

［3］Ma Q L, Zheng Q L, Peng H, et al. Multistepprediction of chaotic time series based on coevolutionary recurrent neural network［J］. Chinese Physics B, 2008, 17(2): 536542.

［4］彭金柱, 王耀南, 王杰. 基于递归模糊神经网络的机器人鲁棒H∞跟踪控制［J］. 控制理论与应用, 2010, 27(9): 11451151.

Peng J Z, Wang Y N, Wang J. Robust Hinfinity trackingcontrol for robotic system based on recurrent fuzzyneuralnetworks［J］. Control Theory & Applications, 2009, 6(4): 684693.

［5］杨臻明, 岳继光, 王晓保, 李晓龙. 基于回归模型的城市轨道交通能耗预测［J］. 城市轨道交通研究, 2010, 13(12): 2225.

Yang Z M, Yue J G, Wang X B, Li X L. Prediction of urban rail transit power consumption based on regression model［J］. Urban Mass Transit, 2010, 13(12):2225.

［6］Zemouri R, Gouriveau R, Zerhouni N. Defining and applying prediction performance metrics on a recurrent NARX time series model［J］. Neurocomputing, 2010, 73(1315): 25062521.

［7］Ludvig O, Nunes U. Novel Maximummargin Training Algorithms for Supervised Neural Networks［J］. IEEE Transactions on Neural Networks, 2010, 21(6): 972984.

［8］Cai X D, Zhang N. Time series prediction with recurrent neural networks trained by a hybrid PSOEA algorithm［J］. Neurocomputing, 2007, 70(1315): 23422353.

基于独立模型的非线性时间序列多步超前预测

Multistep-Ahead Independent Prediction of Nonlinear Time Series Based on Independent Model

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