| 采用优化极限学习机的多变量混沌时间序列预测 |
| |
| 引用本文： | 高光勇,蒋国平.采用优化极限学习机的多变量混沌时间序列预测[J].物理学报,2012,61(4):40506-040506. |
| |
| 作者姓名： | 高光勇 蒋国平 |
| |
| 作者单位： | 1. 南京邮电大学控制与智能技术研究中心,南京210003／九江学院信息科学与技术学院,九江332005
2. 南京邮电大学控制与智能技术研究中心,南京,210003 |
| |
| 基金项目： | 国家自然科学基金(批准号: 60874091), 江苏省‘六大人才高峰’高层次人才项目(批准号: SJ209006), 高等学校博士点基金(批准号: 20103223110003), 江苏省高校基础研究计划 (批准号: 08KJD510022), 江苏省自然科学基金(批准号: BK2010526), 南京邮电大学引进人才项目(批准号:NY209021)和江苏省高校研究生科研创新计划(批准号: CXZZ11_0400)资助的课题. |
| |
| 摘 要： | 基于优化极限学习机理论, 提出一种多变量混沌时间序列预测方法. 该方法利用复合混沌和混沌变尺度算法对极限学习机的模型参数进行搜索和优化, 以提高极限学习机的泛化性能; 然后利用优化后的极限学习机对Rossler耦合系统的多变量混沌时序进行一步和多步预测, 并且与同类算法进行了比较, 结果表明了该方法的有效性, 且算法具有较强的抗噪能力; 最后讨论了预测结果和隐层神经元数目的关系.
|
| 关 键 词： | 极限学习机 多变量时间序列 混沌序列预测 复合混沌优化 |
| 收稿时间： | 2011-04-18 |
| 修稿时间： | 7/6/2011 12:00:00 AM |
Prediction of multivariable chaotic time series using optimized extreme learning machine |
| |
| Gao Guang-Yong and Jiang Guo-Ping.Prediction of multivariable chaotic time series using optimized extreme learning machine[J].Acta Physica Sinica,2012,61(4):40506-040506. |
| |
| Authors: | Gao Guang-Yong and Jiang Guo-Ping |
| |
| Institution: | Center for Control & Intelligence Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; School of Iniformation Science & Technology Jiujiang University, Jiujiang 332005, China;Center for Control & Intelligence Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China |
| |
| Abstract: | A prediction algorithm of multivariable chaotic time series is proposed based on optimized extreme learning machine (ELM). In this algorithm, a presented composite chaos system and mutative scale chaos method are utilized first to search and optimize the parameters of ELM for improving the generalization performance. Then the optimized ELM is used to predict the multivariable chaotic time series of Rossler coupled system for single step and muti-step, and the scheme is compared with the congeneric method, which shows the validity and stronger ability against noise of the developed algorithm. Finally, the relation between prediction result and number of hidden neurons is discussed. |
| |
| Keywords: | extreme learning machine multivariable chaotic time series prediction of chaotic time series composite chaos optimization |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《物理学报》浏览原始摘要信息 |
| 点击此处可从《物理学报》下载免费的PDF全文 |
|
