文献类型：期刊文献

英文题名：Adaptive Robust Stochastic Configuration Networks for Near-Infrared Multivariate Analysis

作者：Li, Yuqiang[1];Du, Wenli[1,2];Wang, Xinjie[3];Yang, Minglei[1];Zhao, Yunmeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Qingyuan Innovat Lab, Quanzhou 362801, Peoples R China;[3]Hangzhou Normal Univ, Sch Informat Sci & Technol, Hangzhou 311121, Peoples R China

年份：2025

卷号：36

期号：8

起止页码：15207

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20250517779100);WOS:【SCI-EXPANDED(收录号:WOS:001408492600001)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB3305900, in part by the National Natural Science Foundation of China (Basic Science Center Program)under Grant 61988101, in part by the Major Program of Qingyuan Innovation Laboratory under Grant 00122002, and in part by the Fundamental Research Funds for the Central Universities

语种：英文

外文关键词：Adaptation models; Incremental learning; Stochastic processes; Robustness; Predictive models; Bayes methods; Analytical models; Learning systems; Convergence; Vectors; Adaptive incremental learning; multivariate modeling; near-infrared (NIR) spectroscopy; sparse Bayesian learning (SBL); stochastic configuration networks (SCNs)

摘要：Near-infrared (NIR) technology has gained wide acceptance in practical processes and is now the measurement of choice in many sectors. However, with increasing spectral dimensionality, it is challenging to establish a prediction model with satisfactory stability and generalization. Stochastic configuration networks (SCNs) based on supervisory learning mechanism have demonstrated significant advantages in developing nonlinear learners. However, existing incremental learning strategies make it difficult to achieve fast convergence while obtaining a suitable-scale network in high-dimensional spectra modeling. In addition, the linear or regularization weight estimation methods are vulnerable to outliers and noise in NIR analysis. To accelerate model construction and improve model performance in high-dimensional spectra analysis, the adaptive robust SCN (AR-SCN) algorithm is proposed in this work, which can perform adaptive incremental learning according to the prediction residual and robustly estimate the output weights by the global-local shrinkage strategy. Comparison results on three benchmark NIR datasets and real-world gasoline blending process verify the effectiveness of the proposed method. Compared with the state-of-the-art SCNs, the AR-SCN method can simultaneously improve the construction efficiency and robustness of SCNs.