A New Distributed Echo State Network Integrated With an Auto-Encoder for Dynamic Soft Sensing
A New Distributed Echo State Network Integrated With an Auto-Encoder for Dynamic Soft Sensing
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DOI：
10.1109/tim.2022.3228278
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发表时间：
2023
影响因子：
5.6
通讯作者：
Yanlin He;Lei Chen;Yuan Xu;Qun Zhu;Sha Lu
中科院分区：
文献类型：
--
作者：
Yanlin He;Lei Chen;Yuan Xu;Qun Zhu;Sha Lu
随着动态工业过程变得越来越复杂，开发精确的软测量变得越来越困难。回声状态网络(ESNs)作为动态神经网络(NN)模型已被广泛应用于动态软测量的开发中。然而，当面对高维数据时，eSNs的输入空间可能会出现维度灾难问题。此外，在eSNs中，储集层节点的数量很大，导致储集层输出之间的共线性。为了解决这些问题，本文提出了一种新的集成自动编码器的分布式ESN模型(AE-DESNm)。特别是，AE-DESNm具有分布式和独立的输入子网。对每个输入子网进行降维，消除了维度灾难问题。为了得到分布独立的AE-DESNm输入子网，采用第一整数近邻聚类层次(Finch)算法对高维数据的输入属性进行聚类。每个集群对应于一个输入子网。此外，为了处理水库输出中的共线性问题，使用自动编码器(AE)从水库输出中提取特征。最后，用极限学习机(ELM)对提取的特征进行建模，以估计关键变量。为了验证AE-DESNm的性能，使用了一个数值算例和一个实际工业生产过程的数据集--精对苯二甲酸(PTA)。实验结果表明，与其他方法相比，所提出的AE-DESNm在动态工业过程软测量建模中具有更高的精度。
As dynamic industrial processes become increasingly complicated, it tends to be difficult to develop accurate soft sensors. Echo state networks (ESNs) as dynamic neural network (NN) models have been broadly used in developing dynamic soft sensors. However, when facing high-dimensional data, the dimension disaster problem might occur in the input space of ESNs. Furthermore, in ESNs, the number of reservoir nodes is large, causing collinearity between the reservoir outputs. To solve these problems, in this article, a new distributed ESN model integrated with auto-encoder (AE-DESNm) is proposed. In particular, AE-DESNm has distributed and independent input subnets. The dimensionality of each input subnet has been reduced, eliminating the dimension disaster problem. To obtain the distributed and independent input subnets of AE-DESNm, the first integer neighbor clustering hierarchy (FINCH) algorithm is used to cluster the input attributes of high-dimensional data. Each cluster corresponds to one input subnet. In addition, seeking to handle the collinearity issue in the reservoir outputs, auto-encoder (AE) is used to extract features from the reservoir outputs. Finally, the extracted features are modeled by an extreme learning machine (ELM) to estimate key variables. To verify the performance of AE-DESNm, a numerical example and a dataset from one real industrial process named purified terephthalic acid (PTA) production are used. Compared with other methods, the experimental results show that the proposed AE-DESNm can achieve much higher accuracy in soft sensor modeling for dynamic industrial processes.