Adaptive Robust Stochastic Configuration Networks for Near-Infrared Multivariate Analysis

作     者：Li, Yuqiang Du, Wenli Wang, Xinjie Yang, Minglei Zhao, Yunmeng 

作者机构：East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Qingyuan Innovat Lab Quanzhou 362801 Peoples R China Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》 (IEEE Trans. Neural Networks Learn. Sys.)

年 卷 期：2025年第36卷第6期

页      面：10544-10557页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2022YFB3305900] National Natural Science Foundation of China (Basic Science Center Program) Major Program of Qingyuan Innovation Laboratory Fundamental Research Funds for the Central Universities 

主　　题：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.