期刊论文

Lu, Xinjiang;Fan, Bin

英文

IEEE Transactions on Systems, Man, and Cybernetics: Systems

2168-2216

2018

50

12

5162-5171

10.1109/TSMC.2018.2867238

Manuscript received July 21, 2018; accepted August 19, 2018. Date of publication September 14, 2018; date of current version November 18, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 51675539, in part by the Project of Innovation-Driven Plan in Central South University under Grant 2015CX002, in part by the Project of State Key Laboratory of High Performance Complex Manufacturing under Grant ZZYJKT2018-17, in part by the Hunan Province Science and Technology Plan under Grant 2016RS2015, and in part by the Central South University Graduate Scientific Research Innovation Project under Grant 2018zzts452. This paper was recommended by Associate Editor C. Zhang. (Corresponding authors: Xinjiang Lu; Bin Fan.) X. Lu, L. Ming, and T. Hu are with the State Key Laboratory of High Performance Complex Manufacturing, School of Mechanical and Electrical Engineering, Central South University, Hunan 410083, China (e-mail: luxj@csu.edu.cn; minglizn@csu.edu.cn; hute2017@csu.edu.cn).

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The least squares support vector machine (LS-SVM) is often employed to model data with a nonlinear distribution using a divide-and-conquer strategy. However, when nonlinear data are contaminated by either noise or outliers, LS-SVM is often an ineffective approach due to a lack of robustness. In this paper, a collaborative learning-based clustered LS-SVM method is proposed for modeling of nonlinear processes that are subject to noise or outliers. First, a large-scale dataset is divided into several subsets and the data distribution of each subset is estimated. A robust LS-SVM is then developed ...