文献类型：期刊文献

英文题名：A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

作者：Bao, Yaoyao[1];Zhu, Yuanming[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：18

起止页码：186

外文期刊名：ENGINEERING

收录：;EI(收录号：20224312994208);WOS:【SCI-EXPANDED(收录号:WOS:000925255000001)】；

基金：This work was supported by the National Key Research and Development Program of China (2016YFB0303401) , the International (Regional) Cooperation and Exchange Project (61720106008) , the National Science Fund for Distinguished Young Scholars (61725301) , and the Shanghai AI Lab.

语种：英文

外文关键词：Local quadratic embedding; Metric learning; Regression machine; Soft sensor

摘要：Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes the local quadratic embedding learning (LQEL) algorithm for regression problems based on metric learning and neural networks (NNs). First, Mahalanobis metric learning is improved by optimizing the global consis-tency of the metrics between instances in the input and output space. Then, we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the con-straints. Based on the hypothesis of local quadratic interpolation, the algorithm introduces two light-weight NNs; one is used to learn the coefficient matrix in the local quadratic model, and the other is implemented for weight assignment for the prediction results obtained from different local neighbors. Finally, the two sub-models are embedded in a unified regression framework, and the parameters are learned by means of a stochastic gradient descent (SGD) algorithm. The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances. Moreover, it pre-vents the model degradation caused by sensor drift and unmeasurable variables by modeling variable dif-ferences with the LQEL algorithm. Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.(c) 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).