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

针对训练样本贯序输入时的极端学习机(ELM)训练问题,提出一种具有选择与遗忘机制的极端学习机(SF-ELM),并研究了其在混沌时间序列预测中的应用.SF-ELM以逐次增加新训练样本的方式实现在线训练,通过引入遗忘因子以减弱旧训练样本的影响,同时以泛化能力为判断依据,对其输出权值进行选择性递推更新.混沌时间序列在线预测实例表明,SF-ELM是一种有效的ELM在线训练模式.相比于在线贯序极端学习机,SF-ELM具有更快的在线训练速度和更高的在线预测精度,因此更适于混沌时间序列在线预测.

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.