A Support Vector Regression Approach for Recursive vskip0.2 baselineskip Simultaneous Data Reconciliation and Gross ErrorDetection in Nonlinear Dynamical Systems

2009 ◽  
Vol 35 (6) ◽  
pp. 707-716 ◽  
Author(s):  
Yu MIAO ◽  
Hong-Ye SU ◽  
Jian CHU
Keyword(s):  
Dynamical Systems ◽  
Support Vector Regression ◽  
Nonlinear Dynamical Systems ◽  
Data Reconciliation ◽  
Support Vector ◽  
Nonlinear Dynamical ◽  
Regression Approach

Soft-constrained Linear Programming Support Vector Regression for Nonlinear Black-box Systems Identification

2008 ◽  
pp. 137-147 ◽  
Author(s):  
Zhao Lu ◽  
Jing Sun
Keyword(s):  
Linear Programming ◽  
Support Vector Regression ◽  
Loss Function ◽  
Nonlinear Dynamical Systems ◽  
Black Box ◽  
Support Vector ◽  
Nonlinear Dynamical ◽  
Systems Identification ◽  
Programming Support ◽  
Sparse Kernel

As an innovative sparse kernel modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In the support vector regression, Vapnik developed the -insensitive loss function as a trade-off between the robust loss function of Huber and one that enables sparsity within the support vectors. The use of support vector kernel expansion provides us a potential avenue to represent nonlinear dynamical systems and underpin advanced analysis. However, in the standard quadratic programming support vector regression (QP-SVR), its implementation is more computationally expensive and enough model sparsity can not be guaranteed. In an attempt to surmount these drawbacks, this article focus on the application of soft-constrained linear programming support vector regression (LP-SVR) in nonlinear black-box systems identification, and the simulation results demonstrates that the LP-SVR is superior to QP-SVR in model sparsity and computational efficiency

Soft-Constrained Linear Programming Support Vector Regression for Nonlinear Black-Box Systems Identification

2011 ◽  
pp. 889-897
Author(s):  
Zhao Lu ◽  
Jing Sun
Keyword(s):  
Linear Programming ◽  
Support Vector Regression ◽  
Loss Function ◽  
Nonlinear Dynamical Systems ◽  
Black Box ◽  
Support Vector ◽  
Nonlinear Dynamical ◽  
Systems Identification ◽  
Programming Support ◽  
Sparse Kernel

As an innovative sparse kernel modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In the support vector regression, Vapnik developed the -insensitive loss function as a trade-off between the robust loss function of Huber and one that enables sparsity within the support vectors. The use of support vector kernel expansion provides us a potential avenue to represent nonlinear dynamical systems and underpin advanced analysis. However, in the standard quadratic programming support vector regression (QP-SVR), its implementation is more computationally expensive and enough model sparsity can not be guaranteed. In an attempt to surmount these drawbacks, this article focus on the application of soft-constrained linear programming support vector regression (LP-SVR) in nonlinear black-box systems identification, and the simulation results demonstrates that the LP-SVR is superior to QP-SVR in model sparsity and computational efficiency

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