基于在线相关熵极限学习机的器件退化趋势实时流预测方法

针对器件退化趋势预测,现有方法难以有效进行模型在线更新且趋势预测易受噪声和奇异值影响产生畸变的问题,构建了一种在线受限相关熵极限学习机,并在该模型基础上,提出了预测模型动态更新方法,完成了高可靠器件退化趋势建模及参数线动态更新。通过建立训练数据误差编码本,利用M-估计动态检测训练数据中的奇异值并进行相应的模型修正,进一步提高模型的鲁棒性。仿真实验及光电耦合器CTR实验均表明该方法相比于典型的预测方法,能够在避免实时噪声和奇异值干扰的情况下对预测模型作出有效的更新且能快速给出单步及多步的预测结果,有效地提升了实时预测的准确性。

Online sequential regularized correntropy criterion extreme learning machine on spark streaming signal prediction for electronic device degradation

For real time prediction on device degradation, the existing algorithms are difficult to update the trained model and the prediction results are easy to be distorted due to the outlier and noise. To solve these problems, a novel method named as online sequential regularized correntropy criterion extreme learning machine (OSRCC ELM) is proposed to generate high robust prediction model and provide dynamic updating ability. Firstly, based on the regularized correntropy criterion ELM, the updating method for the model is realized. Secondly, the outlier and noise are detected with the dynamic M estimator that is integrated with the error codebook. Finally, the corresponding influence of the outlier and noise are removed from the current model. Experiments using simulated data and CTR of optical couples show that OSRCC ELM can achieve higher prediction accuracy without the effect of outliers and noises than other methods while provide accurate prediction with high speed.