[2] [2] FATTAHI S, SOJOUDI S.Data-driven sparse system identification［C］//The 56th Annual Allerton Conference on Communication, Control, and Computing, IEEE, 2018:462-469.

[3] [3] SCHOUKENS J, VAES M, PINTELON R.Linear system identification in a nonlinear setting:nonparametric analysis of the nonlinear distortions and their impact on the best linear approximation［J］.IEEE Control Systems, 2016, 36(3):38-69．

[4] [4] YU C, WANG Q G, ZHANG D, et al.System identification in presence of outliers［J］.IEEE Transactions on Cybernetics, 2015, 46(5):1202-1216.

[5] [5] KHATIBISEPEHR S, HUANG B.A Bayesian approach to robust process identification with ARX models［J］.AIChE Journal, 2013, 59(3): 845-859．

[6] [6] YU J.A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses［J］.Computers & Chemical Engineering, 2012, 41:134-144.

[7] [7] KO C N.Identification of nonlinear systems with outliers using wavelet neural networks based on annealing dynamical learning algorithm［J］.Engineering Applications of Artificial Intelligence, 2012, 25(3):533-543.

[8] [8] SUYKENS J A K, VAN GESTEL T, DE BRABANTER J, et al.Least squares support vector machines［M］.Singapore:World Scientific, 2002.

[12] [12] XU Q F, ZHANG J X, JIANG C X, et al.Weighted quantile regression via support vector machine［J］.Expert Systems with Applications, 2015, 42(13):5441-5451.