Robust stochastic configuration networks for industrial data modelling with Student’s-t mixture distribution
Data collected from industrial sites commonly contains outliers or noise that obey unknown distributions, making it challenging to establish an accurate data-driven model. Therefore, this paper proposes a novel robust stochastic configuration network based on a Student’s-t mixture distribution (term...
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| Published in: | Information sciences Vol. 607; pp. 493 - 505 |
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01.08.2022
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| Abstract | Data collected from industrial sites commonly contains outliers or noise that obey unknown distributions, making it challenging to establish an accurate data-driven model. Therefore, this paper proposes a novel robust stochastic configuration network based on a Student’s-t mixture distribution (termed as SM-RSC). Firstly, a stochastic configuration algorithm is employed to determine the number of hidden nodes, the input weights and biases. Secondly, the maximum a posteriori (MAP) estimate is used to evaluate the output weights of the SCN learner model under the assumption that outliers or noises obey the Student’s-t mixture distribution. Because the output weights cannot be solved directly due to the unknown hyper-parameters of the mixture distribution, we apply the well-known expectation–maximization (EM) algorithm for optimizing the hyper-parameters of the mixture distribution and update the output weights iteratively. The proposed algorithm is evaluated by a function approximation, four benchmark datasets, and a case study on industrial data modelling for a waste incineration process. The results show that SM-RSC performs favorably compared with other methods. |
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| AbstractList | Data collected from industrial sites commonly contains outliers or noise that obey unknown distributions, making it challenging to establish an accurate data-driven model. Therefore, this paper proposes a novel robust stochastic configuration network based on a Student’s-t mixture distribution (termed as SM-RSC). Firstly, a stochastic configuration algorithm is employed to determine the number of hidden nodes, the input weights and biases. Secondly, the maximum a posteriori (MAP) estimate is used to evaluate the output weights of the SCN learner model under the assumption that outliers or noises obey the Student’s-t mixture distribution. Because the output weights cannot be solved directly due to the unknown hyper-parameters of the mixture distribution, we apply the well-known expectation–maximization (EM) algorithm for optimizing the hyper-parameters of the mixture distribution and update the output weights iteratively. The proposed algorithm is evaluated by a function approximation, four benchmark datasets, and a case study on industrial data modelling for a waste incineration process. The results show that SM-RSC performs favorably compared with other methods. |
| Author | Guo, Jingcheng Wang, Dianhui Yan, Aijun |
| Author_xml | – sequence: 1 givenname: Aijun surname: Yan fullname: Yan, Aijun organization: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China – sequence: 2 givenname: Jingcheng surname: Guo fullname: Guo, Jingcheng organization: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China – sequence: 3 givenname: Dianhui surname: Wang fullname: Wang, Dianhui email: dh.wang@deepscn.com organization: Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China |
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| References | Li, Guo, Hou (b0160) 2019; 38 Pao, Takefuji (b0025) 1992; 25 Wang, Cui (b0055) 2017; 417 Lian, Zhang, Li (b0070) 2019; 30 Li, Tao, Li, Chen, Wang (b0065) 2019; 488 Dai, Chen, Chu, Ma, Chai (b0090) 2017; 5 Zhou, Lv, Wang, Chai (b0105) 2017; 64 Shoham (b0135) 2002; 35 Li, Huang, Wang (b0100) 2019; 473 Gorban, Tyukin, Prokhorov, Sofeikov (b0155) 2016; 364–365 Zhang, Chai, Wang (b0015) 2017; 28 Kadlec, Gabrys, Strandt (b0080) 2009; 33 J. Lu, J. Ding, Mixed-distribution based robust stochastic configuration networks for prediction interval construction, IEEE Trans. Indus. Inf., 2020, 16(8): 5099–5019. Dai, Li, Zhou, Chai (b0075) 2019; 484 Huang, Huang, Wang (b0045) 2020; 16 Igelnik, Pao (b0030) 1995; 6 Bi, Yuan, Zhang, Zhang (b0060) 2019; 481 Wang, Li (b0035) 2017; 47 Battiti (b0165) 1994; 5 Zhang, Wu, Nguyen (b0130) 2013; 20 Wang, Li (b0095) 2017; 412 Lu, Ding (b0050) 2019; 486 Phillips (b0145) 2002; 12 Press, Teukolsky, Vetterling, Flannery (b0150) 2007 Chai, Qin, Wang (b0005) 2014; 38 Zhou, Yuan, Wang, Wang, Chai (b0010) 2015; 325 Wang, Wang (b0040) 2020; 32 Zhao, Ma, Huang (b0115) 2019; 15 Peel, McLachlan (b0140) 2000; 10 Li, Zhou, Liu, Wang (b0020) 2020; 17 Huang, Wang, Zheng (b0085) 2014; 88 Chen, Sun, Ling, Ho (b0120) 2020; 14 Guo, Kodamana, Zhao, Huang, Ding (b0125) 2017; 13 Chai (10.1016/j.ins.2022.05.105_b0005) 2014; 38 Huang (10.1016/j.ins.2022.05.105_b0085) 2014; 88 Battiti (10.1016/j.ins.2022.05.105_b0165) 1994; 5 Dai (10.1016/j.ins.2022.05.105_b0090) 2017; 5 Guo (10.1016/j.ins.2022.05.105_b0125) 2017; 13 Igelnik (10.1016/j.ins.2022.05.105_b0030) 1995; 6 Li (10.1016/j.ins.2022.05.105_b0160) 2019; 38 Zhang (10.1016/j.ins.2022.05.105_b0015) 2017; 28 Pao (10.1016/j.ins.2022.05.105_b0025) 1992; 25 Gorban (10.1016/j.ins.2022.05.105_b0155) 2016; 364–365 Zhou (10.1016/j.ins.2022.05.105_b0105) 2017; 64 Zhang (10.1016/j.ins.2022.05.105_b0130) 2013; 20 Lu (10.1016/j.ins.2022.05.105_b0050) 2019; 486 Zhao (10.1016/j.ins.2022.05.105_b0115) 2019; 15 Phillips (10.1016/j.ins.2022.05.105_b0145) 2002; 12 Wang (10.1016/j.ins.2022.05.105_b0055) 2017; 417 Chen (10.1016/j.ins.2022.05.105_b0120) 2020; 14 Wang (10.1016/j.ins.2022.05.105_b0040) 2020; 32 Li (10.1016/j.ins.2022.05.105_b0065) 2019; 488 Bi (10.1016/j.ins.2022.05.105_b0060) 2019; 481 Shoham (10.1016/j.ins.2022.05.105_b0135) 2002; 35 Li (10.1016/j.ins.2022.05.105_b0100) 2019; 473 Dai (10.1016/j.ins.2022.05.105_b0075) 2019; 484 Wang (10.1016/j.ins.2022.05.105_b0095) 2017; 412 Kadlec (10.1016/j.ins.2022.05.105_b0080) 2009; 33 Press (10.1016/j.ins.2022.05.105_b0150) 2007 Wang (10.1016/j.ins.2022.05.105_b0035) 2017; 47 Huang (10.1016/j.ins.2022.05.105_b0045) 2020; 16 Li (10.1016/j.ins.2022.05.105_b0020) 2020; 17 10.1016/j.ins.2022.05.105_b0110 Lian (10.1016/j.ins.2022.05.105_b0070) 2019; 30 Zhou (10.1016/j.ins.2022.05.105_b0010) 2015; 325 Peel (10.1016/j.ins.2022.05.105_b0140) 2000; 10 |
| References_xml | – volume: 25 start-page: 76 year: 1992 end-page: 79 ident: b0025 article-title: Functional-link net computing: theory, system architecture, and functionalities publication-title: IEEE Comput. – volume: 20 start-page: 117 year: 2013 end-page: 120 ident: b0130 article-title: A robust fuzzy algorithm based on Student’s t-distribution and mean template for image segmentation application publication-title: IEEE Signal Process Lett. – volume: 38 start-page: 746 year: 2019 end-page: 759 ident: b0160 article-title: Progress on the emission and formation mechanisms of dioxin during the solid waste incineration process publication-title: Environ. Chem. – volume: 88 start-page: 165 year: 2014 end-page: 173 ident: b0085 article-title: The M-estimator for functional linear regression model publication-title: Statistics & Probability Lett. – volume: 15 start-page: 139 year: 2019 end-page: 147 ident: b0115 article-title: Robust FIR state estimation of dynamic processes corrupted by outliers publication-title: IEEE Trans. Ind. Inf. – volume: 412 start-page: 210 year: 2017 end-page: 222 ident: b0095 article-title: Robust stochastic configuration networks with kernel density estimation for uncertain data regression publication-title: Inf. Sci. – year: 2007 ident: b0150 article-title: Number Recipes: The Art of Scientific Computing (Third Edition) – volume: 6 start-page: 1320 year: 1995 end-page: 1329 ident: b0030 article-title: Stochastic choice of basis functions in adaptive function approximation and the functional-link net publication-title: IEEE Trans. Neural Networks – volume: 473 start-page: 73 year: 2019 end-page: 86 ident: b0100 article-title: Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression publication-title: Inf. Sci. – volume: 64 start-page: 7141 year: 2017 end-page: 7151 ident: b0105 article-title: Data-driven robust RVFLNs modeling of a blast furnace iron-making process using Cauchy distribution weighted M-estimation publication-title: IEEE Trans. Ind. Electron. – volume: 30 start-page: 2324 year: 2019 end-page: 2335 ident: b0070 article-title: Integrated sliding mode control and neural networks based packet disordering prediction for nonlinear networked control systems publication-title: IEEE Trans. Neural Networks Learn. Syst. – volume: 486 start-page: 119 year: 2019 end-page: 132 ident: b0050 article-title: Construction of prediction intervals for carbon residual of crude oil based on deep stochastic configuration networks publication-title: Inf. Sci. – volume: 5 start-page: 537 year: 1994 end-page: 550 ident: b0165 article-title: Using mutual information for selecting features in supervised neural net learning publication-title: IEEE Trans. Neural Networks – volume: 35 start-page: 1127 year: 2002 end-page: 1142 ident: b0135 article-title: Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions publication-title: Pattern Recogn. – volume: 38 start-page: 81 year: 2014 end-page: 92 ident: b0005 article-title: Optimal operational control for complex industrial processes publication-title: Ann. Rev. Control – volume: 47 start-page: 3346 year: 2017 end-page: 3479 ident: b0035 article-title: Stochastic configuration networks: fundamentals and algorithms publication-title: IEEE Trans. Cybern. – volume: 364–365 start-page: 129 year: 2016 end-page: 145 ident: b0155 article-title: Approximation with random bases: Pro et Contra publication-title: Inf. Sci. – volume: 10 start-page: 339 year: 2000 end-page: 348 ident: b0140 article-title: Robust mixture modelling using the t distribution publication-title: Stat. Comput. – volume: 32 start-page: 13625 year: 2020 end-page: 13638 ident: b0040 article-title: Prediction of component concentrations in sodium aluminate liquor using stochastic configuration networks publication-title: Neural Comput. Appl. – volume: 5 start-page: 16162 year: 2017 end-page: 16172 ident: b0090 article-title: Robust regularized random vector functional link network and its industrial application publication-title: IEEE Access – volume: 16 start-page: 373 year: 2020 end-page: 383 ident: b0045 article-title: Stochastic configuration networks based adaptive storage replica management for power big data processing publication-title: IEEE Trans. Ind. Inf. – volume: 325 start-page: 237 year: 2015 end-page: 255 ident: b0010 article-title: Multivariable dynamic modeling for molten iron quality using online sequential random vector functional-link networks with self-feedback connections publication-title: Inf. Sci. – volume: 417 start-page: 55 year: 2017 end-page: 71 ident: b0055 article-title: Stochastic configuration networks ensemble with heterogeneous features for large-scale data analytics publication-title: Inf. Sci. – volume: 28 start-page: 1606 year: 2017 end-page: 1617 ident: b0015 article-title: An alternating identification algorithm for a class of nonlinear dynamical systems publication-title: IEEE Trans. Neural Networks Learn. Syst. – volume: 488 start-page: 1 year: 2019 end-page: 12 ident: b0065 article-title: Greengage grading using stochastic configuration networks and a semi-supervised feedback mechanism publication-title: Inf. Sci. – volume: 14 start-page: 771 year: 2020 end-page: 781 ident: b0120 article-title: Robust power system state estimation using t-distribution noise model publication-title: IEEE Syst. J. – volume: 13 start-page: 3047 year: 2017 end-page: 3057 ident: b0125 article-title: Robust identification of nonlinear errors-in-variables systems with parameter uncertainties using variational Bayesian approach publication-title: IEEE Trans. Ind. Inf. – volume: 484 start-page: 367 year: 2019 end-page: 386 ident: b0075 article-title: Stochastic configuration networks with block increments for data modeling in process industries publication-title: Inf. Sci. – volume: 481 start-page: 57 year: 2019 end-page: 68 ident: b0060 article-title: SGW-SCN: An integrated machine learning approach for workload forecasting in geo-distributed cloud data centers publication-title: Inf. Sci. – reference: J. Lu, J. Ding, Mixed-distribution based robust stochastic configuration networks for prediction interval construction, IEEE Trans. Indus. Inf., 2020, 16(8): 5099–5019. – volume: 12 start-page: 281 year: 2002 end-page: 285 ident: b0145 article-title: Least absolute deviations estimation via the EM algorithm publication-title: Stat. Comput. – volume: 33 start-page: 795 year: 2009 end-page: 814 ident: b0080 article-title: Data-driven Soft Sensors in the process industry publication-title: Comput. Chem. Eng. – volume: 17 start-page: 633 year: 2020 end-page: 645 ident: b0020 article-title: Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process publication-title: IEEE Trans. Autom. Sci. Eng. – volume: 5 start-page: 537 issue: 4 year: 1994 ident: 10.1016/j.ins.2022.05.105_b0165 article-title: Using mutual information for selecting features in supervised neural net learning publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.298224 – volume: 5 start-page: 16162 issue: 8 year: 2017 ident: 10.1016/j.ins.2022.05.105_b0090 article-title: Robust regularized random vector functional link network and its industrial application publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2737459 – volume: 14 start-page: 771 issue: 1 year: 2020 ident: 10.1016/j.ins.2022.05.105_b0120 article-title: Robust power system state estimation using t-distribution noise model publication-title: IEEE Syst. J. doi: 10.1109/JSYST.2018.2890106 – volume: 88 start-page: 165 issue: 5 year: 2014 ident: 10.1016/j.ins.2022.05.105_b0085 article-title: The M-estimator for functional linear regression model publication-title: Statistics & Probability Lett. doi: 10.1016/j.spl.2014.01.016 – volume: 325 start-page: 237 issue: 35 year: 2015 ident: 10.1016/j.ins.2022.05.105_b0010 article-title: Multivariable dynamic modeling for molten iron quality using online sequential random vector functional-link networks with self-feedback connections publication-title: Inf. Sci. doi: 10.1016/j.ins.2015.07.002 – volume: 38 start-page: 81 issue: 1 year: 2014 ident: 10.1016/j.ins.2022.05.105_b0005 article-title: Optimal operational control for complex industrial processes publication-title: Ann. Rev. Control doi: 10.1016/j.arcontrol.2014.03.005 – volume: 38 start-page: 746 issue: 4 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0160 article-title: Progress on the emission and formation mechanisms of dioxin during the solid waste incineration process publication-title: Environ. Chem. – volume: 28 start-page: 1606 issue: 7 year: 2017 ident: 10.1016/j.ins.2022.05.105_b0015 article-title: An alternating identification algorithm for a class of nonlinear dynamical systems publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2016.2547968 – volume: 6 start-page: 1320 issue: 6 year: 1995 ident: 10.1016/j.ins.2022.05.105_b0030 article-title: Stochastic choice of basis functions in adaptive function approximation and the functional-link net publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.471375 – volume: 488 start-page: 1 issue: 19 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0065 article-title: Greengage grading using stochastic configuration networks and a semi-supervised feedback mechanism publication-title: Inf. Sci. – volume: 17 start-page: 633 issue: 2 year: 2020 ident: 10.1016/j.ins.2022.05.105_b0020 article-title: Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2019.2939052 – volume: 33 start-page: 795 issue: 4 year: 2009 ident: 10.1016/j.ins.2022.05.105_b0080 article-title: Data-driven Soft Sensors in the process industry publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2008.12.012 – volume: 32 start-page: 13625 issue: 17 year: 2020 ident: 10.1016/j.ins.2022.05.105_b0040 article-title: Prediction of component concentrations in sodium aluminate liquor using stochastic configuration networks publication-title: Neural Comput. Appl. doi: 10.1007/s00521-020-04771-4 – volume: 10 start-page: 339 issue: 4 year: 2000 ident: 10.1016/j.ins.2022.05.105_b0140 article-title: Robust mixture modelling using the t distribution publication-title: Stat. Comput. doi: 10.1023/A:1008981510081 – volume: 481 start-page: 57 issue: 12 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0060 article-title: SGW-SCN: An integrated machine learning approach for workload forecasting in geo-distributed cloud data centers publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.12.027 – volume: 412 start-page: 210 issue: 10 year: 2017 ident: 10.1016/j.ins.2022.05.105_b0095 article-title: Robust stochastic configuration networks with kernel density estimation for uncertain data regression publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.05.047 – volume: 20 start-page: 117 issue: 2 year: 2013 ident: 10.1016/j.ins.2022.05.105_b0130 article-title: A robust fuzzy algorithm based on Student’s t-distribution and mean template for image segmentation application publication-title: IEEE Signal Process Lett. doi: 10.1109/LSP.2012.2230626 – volume: 15 start-page: 139 issue: 1 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0115 article-title: Robust FIR state estimation of dynamic processes corrupted by outliers publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2018.2829167 – volume: 486 start-page: 119 issue: 17 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0050 article-title: Construction of prediction intervals for carbon residual of crude oil based on deep stochastic configuration networks publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.02.042 – volume: 47 start-page: 3346 issue: 10 year: 2017 ident: 10.1016/j.ins.2022.05.105_b0035 article-title: Stochastic configuration networks: fundamentals and algorithms publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2017.2734043 – volume: 64 start-page: 7141 issue: 9 year: 2017 ident: 10.1016/j.ins.2022.05.105_b0105 article-title: Data-driven robust RVFLNs modeling of a blast furnace iron-making process using Cauchy distribution weighted M-estimation publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2686369 – volume: 35 start-page: 1127 issue: 5 year: 2002 ident: 10.1016/j.ins.2022.05.105_b0135 article-title: Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions publication-title: Pattern Recogn. doi: 10.1016/S0031-3203(01)00080-2 – volume: 484 start-page: 367 issue: 5 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0075 article-title: Stochastic configuration networks with block increments for data modeling in process industries publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.01.062 – volume: 12 start-page: 281 issue: 3 year: 2002 ident: 10.1016/j.ins.2022.05.105_b0145 article-title: Least absolute deviations estimation via the EM algorithm publication-title: Stat. Comput. doi: 10.1023/A:1020759012226 – year: 2007 ident: 10.1016/j.ins.2022.05.105_b0150 – ident: 10.1016/j.ins.2022.05.105_b0110 doi: 10.1109/TII.2019.2954351 – volume: 30 start-page: 2324 issue: 8 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0070 article-title: Integrated sliding mode control and neural networks based packet disordering prediction for nonlinear networked control systems publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2018.2873183 – volume: 13 start-page: 3047 issue: 6 year: 2017 ident: 10.1016/j.ins.2022.05.105_b0125 article-title: Robust identification of nonlinear errors-in-variables systems with parameter uncertainties using variational Bayesian approach publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2017.2712743 – volume: 16 start-page: 373 issue: 1 year: 2020 ident: 10.1016/j.ins.2022.05.105_b0045 article-title: Stochastic configuration networks based adaptive storage replica management for power big data processing publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2019.2919268 – volume: 473 start-page: 73 issue: 4 year: 2019 ident: 10.1016/j.ins.2022.05.105_b0100 article-title: Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression publication-title: Inf. Sci. – volume: 25 start-page: 76 issue: 5 year: 1992 ident: 10.1016/j.ins.2022.05.105_b0025 article-title: Functional-link net computing: theory, system architecture, and functionalities publication-title: IEEE Comput. doi: 10.1109/2.144401 – volume: 417 start-page: 55 issue: 31 year: 2017 ident: 10.1016/j.ins.2022.05.105_b0055 article-title: Stochastic configuration networks ensemble with heterogeneous features for large-scale data analytics publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.07.003 – volume: 364–365 start-page: 129 issue: 29 year: 2016 ident: 10.1016/j.ins.2022.05.105_b0155 article-title: Approximation with random bases: Pro et Contra publication-title: Inf. Sci. doi: 10.1016/j.ins.2015.09.021 |
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