We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Non-Linear Feedback Neural Network for Solution of Quadratic Programming Problems

by Mohd. Samar Ansari, Syed Atiqur Rahman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 2
Year of Publication: 2012
Authors: Mohd. Samar Ansari, Syed Atiqur Rahman
10.5120/4796-7049

Mohd. Samar Ansari, Syed Atiqur Rahman . Non-Linear Feedback Neural Network for Solution of Quadratic Programming Problems. International Journal of Computer Applications. 39, 2 ( February 2012), 44-48. DOI=10.5120/4796-7049

@article{ 10.5120/4796-7049,
author = { Mohd. Samar Ansari, Syed Atiqur Rahman },
title = { Non-Linear Feedback Neural Network for Solution of Quadratic Programming Problems },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 2 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number2/4796-7049/ },
doi = { 10.5120/4796-7049 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:26.059912+05:30
%A Mohd. Samar Ansari
%A Syed Atiqur Rahman
%T Non-Linear Feedback Neural Network for Solution of Quadratic Programming Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 2
%P 44-48
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a recurrent neural circuit for solving quadratic programming problems. The objective is tominimize a quadratic cost function subject to linearconstraints. The proposed circuit employs non-linearfeedback, in the form of unipolar comparators, to introducetranscendental terms in the energy function ensuring fastconvergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit comparesfavorably with other proposed circuits for the same task. PSPICE simulation results arepresented for a chosen optimization problem and are foundto agree with the algebraic solution.

References
  1. Atkociunas, J. 1996. Quadratic programming for degenerate shakedown problems of bar structures. Mechanics Research Communications, 23(2), 195–206.
  2. Bartlett, R.A., Wachter, A., and Biegler, L.T. 2000. Active set vs. interior point strategies for model predictive control. In Proceedings of the American Control Conference, Chicago, USA, June 2000, 4229–4233.
  3. Maier, G. and Munro, J. 1982. Mathematical programming applications to engineering plastic analysis. Applied Mechanics Reviews, 35, 1631–1643.
  4. Nordebo, S., Zang, Z., and Claesson, I. 2001. A semi-in?nite quadratic programming algorithm with applications to array pattern synthesis. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 48(3), 225–232.
  5. Schonherr, S. 2002. Quadratic programming in geometric optimization: theory, implementation, and applications. Technical report, Swiss Federal Institute of Technology, Zurich.
  6. Borguet, S. and O. Lonard, O. 2009. A quadratic programming framework for constrained and robust jet engine health monitoring. Progress in Propulsion Physics, 1, 669–692.
  7. Dembo, R.S. and Tulowitzki, U. 1988. Computing equilibria on large multicommodity networks: An application of truncated quadratic programming algorithms. Networks, 18(4), 273–284.
  8. Krogh, A. 2008. What are arti?cial neural networks? Nature Biotechnology, 26(2), 195–197.
  9. Tank, D.W. and Hop?eld, J. 1986. Simple ‘neural’ optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit. IEEE Transactions on Circuits and Systems, 33(5), 533–541.
  10. Kennedy, M.P. and Chua, L.O. 1988. Neural networks for nonlinear programming. IEEE Transactions on Circuits and Systems, 35(5), 554–562.
  11. Maa, C.-Y. andShanblatt, M.A. 1992. Linear and quadratic programming neural network analysis. IEEE Transactions on Neural Networks, 3(4), 580–594.
  12. Chen, Y.-H. and Fang, S.-C. 1998. Solving convex programming problems with equality constraints by neural networks. Computers & Mathematics with Applications, 36(7), 41–68.
  13. Wu, X.-Y., Xia, Y.-S., Li, J., and Chen, W.-K. 1996. A high-performance neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks, 7(3), 643–651.
  14. Xia Y. 1996. A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks, 7(6), 1544–1548.
  15. Malek, A. and Alipour, M. 2007. Numerical solution for linear and quadratic programming problems using a recurrent neural network. Applied Mathematics and Computation, 192(1), 27–39.
  16. Rahman, S.A. and Ansari, M.S. 2011. A neural circuitwith transcendental energy function for solving system of linear equations. Analog Integrated Circuits and Signal Processing, 66, 433–440.
  17. Ansari, M.S. and Rahman, S.A. 2010. A DVCC-based non-linear analog circuit for solving linear programming problems. In Proceedings of International Conference on Power, Control and Embedded Systems (ICPCES), Dec 2010, 1–4.
  18. Wang, J. 1992. Recurrent neural network for solving quadratic programming problems with equality constraints. Electronics Letters, 28(14), 1345–1347.
  19. Forti, M. and Tesi, A. New conditions for global stability of neural networks with application to linear and quadratic programming problems. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 42(7), 354–366.
  20. Tao, Q., Cao, J., and Sun, D. 2001. A simple and high performance neural network for quadratic programming problems. Applied Mathematics and Computation, 124(2), 251–260.
  21. Liu, Q. and Wang, J. 2008. A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming. IEEE Transactions on Neural Networks, 19(4), 558–570.
  22. Gould, N.I.M., and Toint, P.L. 2010. A quadratic programming bibliography. Technical report, RAL Numerical Analysis Group, March 2010.
  23. Rahman, S.A., Jayadeva, and S.C. Dutta Roy. 1999. Neural network approach to graph colouring. Electronics Letters, 35(14), 1173–1175.
  24. Newcomb, R.W. and Lohn, J.D. 1998. The handbook of brain theory and neural networks. Chapter: Analog VLSI for neural networks, MIT Press, Cambridge, MA, USA, 86–90.
  25. Ansari, M.S. and Rahman, S.A. 2009. A novel current-mode non-linear feedback neural circuit for solving linear equations. In Proceedings of International Conference on Multimedia, Signal Processing and Communication Technologies, 284–287.
Index Terms

Computer Science
Information Sciences

Keywords

Dynamical Systems Non-Linear Synapse Feedback Networks.