[1]马军海,盛昭瀚.低维混沌时序的预测方法及其应用研究[J].东南大学学报(自然科学版),1999,29(5):65-69.[doi:10.3969/j.issn.1001-0505.1999.05.013]
 Ma Junhai,Sheng Zhaohan.Study on the Prediction Method and Application about Low-dimension Time Series Arising from the Intrinsic Nonlinear Dynamics[J].Journal of Southeast University (Natural Science Edition),1999,29(5):65-69.[doi:10.3969/j.issn.1001-0505.1999.05.013]
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低维混沌时序的预测方法及其应用研究()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
29
期数:
1999年第5期
页码:
65-69
栏目:
数学、物理学、力学
出版日期:
1999-09-20

文章信息/Info

Title:
Study on the Prediction Method and Application about Low-dimension Time Series Arising from the Intrinsic Nonlinear Dynamics
作者:
马军海 盛昭瀚
东南大学管理学院, 南京 210096
Author(s):
Ma Junhai Sheng Zhaohan
Institute of Systems Engineering, Southeast University, Nanjing 210096
关键词:
非线性 混沌模型 参数识别 时序预测
Keywords:
non-linear chaotic model parameter identification time series prediction
分类号:
O175.14;O241.81
DOI:
10.3969/j.issn.1001-0505.1999.05.013
摘要:
研究由低维非线性动力系统所产生的混沌时序的预测方法及其应用.通过改进的最小二乘方法来估计模型的参数, 并在其相空间中对时序的未来值进行预测.给出了有代表性的实例对本文的模型和算法进行验证. 结果发现选取最佳的模型阶数能增加预测的准确程度, 且混沌时序不可能进行长期的预测. 算例表明本文建议的方法是有效的.
Abstract:
We consider the problem of prediction and system identification for low-dimension time series that arise from the intrinsic nonlinear dynamics of the system. We give a procedure for constructing parameterized maps which evolve points in the phase space into the future. The parameter values are chosen through the improved constrained least-squares optimization procedure. We also discuss the motivation and methods we utilize for choosing the form of our parametric maps. We give detailed examples to testify the algorithm in this paper. We find we are able to select the optimal rank of the model that can increase the precision of prediction, and nonlinear chaotic models can not provide long period superior predictions. 

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金资助项目(69874004).
第一作者:男, 1965年生, 博士后,副教授.
更新日期/Last Update: 1999-09-20