April  2014, 10(2): 621-636. doi: 10.3934/jimo.2014.10.621

LS-SVM approximate solution for affine nonlinear systems with partially unknown functions

1. 

Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China, China, China

2. 

Department of Computing, Curtin University of Technology, Perth, WA 6102

Received  January 2013 Revised  June 2013 Published  October 2013

By using the Least Squares Support Vector Machines (LS-SVMs), we develop a numerical approach to find an approximate solution for affine nonlinear systems with partially unknown functions. This approach can obtain continuous and differential approximate solutions of the nonlinear differential equations, and can also identify the unknown nonlinear part through a set of measured data points. Technically, we first map the known part of the affine nonlinear systems into high dimensional feature spaces and derive the form of approximate solution. Then the original problem is formulated as an approximation problem via kernel trick with LS-SVMs. Furthermore, the approximation of the known part can be expressed via some linear equations with coefficient matrices as coupling square matrices, and the unknown part can be identified by its relationship to the known part and the approximate solution of affine nonlinear systems. Finally, several examples for different systems are presented to illustrate the validity of the proposed approach.
Citation: Guoshan Zhang, Shiwei Wang, Yiming Wang, Wanquan Liu. LS-SVM approximate solution for affine nonlinear systems with partially unknown functions. Journal of Industrial & Management Optimization, 2014, 10 (2) : 621-636. doi: 10.3934/jimo.2014.10.621
References:
[1]

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P. Ramuhalli, L. Udpa and S. S. Udpa, Finite-element neural networks for solving differential equations,, IEEE Transactions on Neural Networks, 16 (2005), 1381.  doi: 10.1109/TNN.2005.857945.  Google Scholar

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J. A. K. Suykens, T. V. Gestel, J. Brabanter,B. D. Moor and J. Vandewalle, Least Squares Support Vector Machines,, 1st edition, (2002).   Google Scholar

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J. A. K. Suykens, J. Vandewalle and B. D. Moor, Optimal control by least squares support vector machines,, Neural Networks, 14 (2001), 23.  doi: 10.1016/S0893-6080(00)00077-0.  Google Scholar

[16]

I. G. Tsoulos, D. Gavrilis and E. Glavas, Solving differential equations with constructed neural networks,, Neurocomputing, 72 (2009), 2385.  doi: 10.1016/j.neucom.2008.12.004.  Google Scholar

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show all references

References:
[1]

A. Akyyuz-Dascioglu and H. Cerdik-Yaslan, The solution of high-order nonlinear ordinary differential equations by Chebyshev Series,, Applied Mathematics and Computation, 217 (2011), 5658.  doi: 10.1016/j.amc.2010.12.044.  Google Scholar

[2]

S. J. An, W. Q. Liu and S. Venkatesh, Fast Exact cross-validation of least squares support vector machines,, Pattern Recognition, 40 (2007), 2154.   Google Scholar

[3]

T. Falck, K. Pelckmans, J. A. K. Suykens and B. De Moor, Identification of Wiener-Hammerstein Systems using LS-SVMs,, 15th IFAC Symposium on System Identification, (2009).   Google Scholar

[4]

Z. Guan and J. F. Lu, Basic of Numerical Analysis(Chinese),, 2nd edition, (2010).   Google Scholar

[5]

A. Isidori, Nonlinear Control Systems: An Introduction,, 3rd edition, (1995).   Google Scholar

[6]

D. R. Kincaid and E. W. Cheney, Numerical Analysis: Mathematics of Scientific Computing,, 3rd edition, (2002).   Google Scholar

[7]

I. E. Lagaris, A. Likas and D. I. Fotiadis, Artificial neural networks for solving ordinary and partial differential equations,, IEEE Transactions on Neural Networks, 9 (1998), 987.  doi: 10.1109/72.712178.  Google Scholar

[8]

H. Lee and I. S. Kang, Neural algorithm for solving differential equations,, Journal of Computational Physics, 91 (1990), 110.  doi: 10.1016/0021-9991(90)90007-N.  Google Scholar

[9]

K. S. McFall and J. R. Mahan, Artificial neural network method for solution of boundary value problems with exact satisfaction of arbitrary boundary conditions,, IEEE Transactions on Neural Networks, 20 (2009), 1221.  doi: 10.1109/TNN.2009.2020735.  Google Scholar

[10]

S. Mehrkanoon, T. Falck and J. A. K. Suykens, Approximate solutions to ordinary differential equations using least squares support vector machines,, IEEE Trans. on Neural Networks and Learning Systems, 23 (2012), 1356.  doi: 10.1109/TNNLS.2012.2202126.  Google Scholar

[11]

M. Popescu, On minimum quadratic functional control of affine nonlinear systems,, Nonlinear Analysis: Theory, 56 (2004), 1165.  doi: 10.1016/j.na.2003.11.009.  Google Scholar

[12]

J. I. Ramos, Linearization techniques for singular initial-value problems of ordinary differential equations,, Applied Mathematics and Computation, 161 (2005), 525.  doi: 10.1016/j.amc.2003.12.047.  Google Scholar

[13]

P. Ramuhalli, L. Udpa and S. S. Udpa, Finite-element neural networks for solving differential equations,, IEEE Transactions on Neural Networks, 16 (2005), 1381.  doi: 10.1109/TNN.2005.857945.  Google Scholar

[14]

J. A. K. Suykens, T. V. Gestel, J. Brabanter,B. D. Moor and J. Vandewalle, Least Squares Support Vector Machines,, 1st edition, (2002).   Google Scholar

[15]

J. A. K. Suykens, J. Vandewalle and B. D. Moor, Optimal control by least squares support vector machines,, Neural Networks, 14 (2001), 23.  doi: 10.1016/S0893-6080(00)00077-0.  Google Scholar

[16]

I. G. Tsoulos, D. Gavrilis and E. Glavas, Solving differential equations with constructed neural networks,, Neurocomputing, 72 (2009), 2385.  doi: 10.1016/j.neucom.2008.12.004.  Google Scholar

[17]

V. Vapnik, The Nature of Statistical Learning Theory,, 1st edition, ().   Google Scholar

[18]

A. M. Wazwaz, A new method for solving initial value problems in second-order ordinary differential equations,, Applied Mathematics and Computation, 128 (2002), 45.  doi: 10.1016/S0096-3003(01)00021-2.  Google Scholar

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