计算机科学
噪音(视频)
水准点(测量)
系列(地层学)
时间序列
回声状态网络
卡尔曼滤波器
扩展卡尔曼滤波器
最大化
算法
国家(计算机科学)
期望最大化算法
循环神经网络
人工智能
人工神经网络
机器学习
数学优化
数学
最大似然
统计
古生物学
图像(数学)
生物
大地测量学
地理
作者
Ying Liu,Long Chen,Yunchong Li,Jun Zhao,Wei Wang
标识
DOI:10.1016/j.eswa.2023.119591
摘要
Industrial time series data usually have a high noise. In this paper, an echo state network (ESN) model with input noise is proposed to address the problem of predicting time series with noise. In the ESN, the introduction of the input noise makes it difficult to accurately estimate the non-linear states of the dynamical reservoir, therefore, in this study, the states are approximated by linearizing it through an extended Kalman filter (EKF). For the learning of the model parameters, the expectation maximization algorithm (EM) is used to iteratively update all the uncertain parameters to construct the prediction intervals, where the state estimation is performed using a forward back algorithm. To verify the effectiveness of the proposed method, two benchmark data sets and three real gas data sets from steel enterprises are used in this paper. Experimental results show that the prediction accuracy of the proposed method is better than that of the existing methods.
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