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.
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.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2012年第4期37-45,共9页
Acta Physica Sinica
基金
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)
江苏省高校研究生科研创新计划(批准号:CXZZ11_0400)资助的课题~~
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
extreme learning machine, multivariable chaotic time series, prediction of chaotic time series, composite chaos optimization
