物理学报2011，Vol.60Issue(8)：68-74,7.
具有选择与遗忘机制的极端学习机在时间序列预测中的应用
Selective forgetting extreme learning machine and its application to time series prediction
摘要
Abstract
To solve the problem of extreme learning machine （ELM） on-line training with sequential training samples,a new algorithm called selective forgetting extreme learning machine （SF-ELM） is proposed and applied to chaotic time series prediction.The SF-ELM adopts the latest training sample and weights the old training samples iteratively to insure that the influence of the old training samples is weakened.The output weight of the SF-ELM is determined recursively during on-line training procedure according to its generalization performance.Numerical experiments on chaotic time series on-line prediction indicate that the SF-ELM is an effective on-line training version of ELM.In comparison with on-line sequential extreme learning machine,the SF-ELM has better performance in the sense of computational cost and prediction accuracy.关键词
混沌时间序列/时间序列预测/神经网络/极端学习机Key words
chaotic time series/time series prediction/neural networks/extreme learning machine分类
信息技术与安全科学引用本文复制引用
张弦,王宏力..具有选择与遗忘机制的极端学习机在时间序列预测中的应用[J].物理学报,2011,60(8):68-74,7.基金项目
国防科技预研基金（批准号：51309060302）资助的课题 （）
