Nonlinear Slow Feature Analysis for Oscillating Characteristics Under Deep Encoder-Decoder Framework

作     者：Puli, Vamsi Krishna Huang, Biao 

作者机构：Univ Alberta Dept Chem & Mat Engn Edmonton T6G 2V4 AB Canada 

出 版 物：《IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS》 (IEEE Trans. Ind. Inf.)

年 卷 期：2024年第20卷第7期

页      面：9568-9578页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Sciences and Engineering Research Council of Canada (NSERC) [ALLRP 561080-20] 

主　　题：Bayesian inference nonstationarity semisupervised learning slow feature analysis soft-sensing variational autoencoder 

摘      要：Slow feature analysis aims to linearly transform measured data into uncorrelated signals that vary from slow to fast. While earlier extensions successfully extracted slow features from nonlinear sequential data, they lacked a modeling preference for nonstationary and oscillating features due to constraints on the prior distribution. To address this limitation, a semisupervised encoder-decoder architecture is proposed in this article, integrating a statistical preference for such characteristics. This regularization is achieved by introducing a first-order autoregressive Gaussian prior within a regular variational auto-encoder framework, as opposed to the standard Gaussian distribution. The evidence lower bound associated with the proposed model is derived using the variational Bayesian inference, and the model parameters are estimated iteratively. The effectiveness of the proposed approach is evaluated on both simulated and real industrial processes.