具有广义正则化与遗忘机制的在线贯序超限学习机

Online sequential extreme learning machine with generalized regularization and forgetting mechanism

针对非平稳时间序列预测问题,提出一种具有广义正则化与遗忘机制的在线贯序超限学习机算法.该算法以增量学习新样本的方式实现在线学习,以遗忘旧的失效样本的方式增强对非平稳系统的动态跟踪能力,并通过引入一种广义的l2正则化使其具有持续的正则化功能,从而保证算法的持续稳定性.仿真实例表明,所提出算法具有较同类算法更好的稳定性和更小的预测误差,适用于具有动态变化特性的非平稳时间序列在线建模与预测.

To solve the prediction problem of nonstationary time series, this paper proposes an online sequential extreme learning machine with forgetting and generalized regularization（OSELM-FGR）. The proposed OSELM-FGR is able to learn the newly arrived samples incrementally by a recursive fashion, and has the improved ability to track the dynamic behavior of time-varying systems by forgetting the outdated samples in the learning process. Moreover, a generalized l2 regularization is introduced into the OSELM-FGR to make the proposed algorithm have a persistent stability. Detailed performance comparisons of the OSELM-FGR with its counterparts are carried out. The experimental results show that,the proposed OSELM-FGR has better performance in the sense of stability and prediction accuracy, which can be applied to the online modeling and prediction of nonstationary time series with dynamic changes.