作者
Zhengwei Hu, Jingchao Peng, Haitao Zhao
发表日期
2021/5
期刊
International Journal of Machine Learning and Cybernetics
卷号
12
页码范围
1501-1516
出版商
Springer Berlin Heidelberg
简介
Dynamic principal component analysis (DPCA) and its nonlinear extension, dynamic kernel principal component analysis (DKPCA), are widely used in the monitoring of dynamic multivariate processes. In traditional DPCA and DKPCA, extended vectors through concatenating current process data point and a certain number of previous process data points are utilized for feature extraction. The dynamic relations among different variables are fixed in the extended vectors, i.e. the adoption of the dynamic information is not adaptively learned from raw process data. Although DKPCA utilizes a kernel function to handle dynamic and (or) nonlinear information, the prefixed kernel function and the associated parameters cannot be most effective for characterizing the dynamic relations among different process variables. To address these problems, this paper proposes a novel nonlinear dynamic method, called …
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Z Hu, J Peng, H Zhao - International Journal of Machine Learning and …, 2021