Fast training of multilayer perceptrons

IEEE Transactions on Neural Networks

Volumn 8, Issue 6, 1997, Pages 1314-1320

  Verma, Brijesh ^a,b  

^a IEEE   (Australia)

^b GRIFFITH UNIVERSITY   (Australia)

Author keywords

Artificial neural networks; Backpropagation algorithm; Local minima; Multilayer perceptrons; Supervised learning

Indexed keywords

BACKPROPAGATION; ERROR COMPENSATION; FUNCTIONS; ITERATIVE METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINEARIZATION; MATHEMATICAL TRANSFORMATIONS; MATRIX ALGEBRA;

BACKPROPAGATION ALGORITHM; ERROR BACKPROPAGATION; INVERSE TRANSFORMATION; LOCAL MINIMA; SUPERVISED LEARNING;

NEURAL NETWORKS;

EID: 0031276994     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.641454     Document Type: Article

References (20)

1. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representation by error propagation," in Parallel Distributed Processing, vol. 1. Cambridge, MA: MIT Press, 1986.
2. J. Hertz and A. Krogh, Introduction to the Theory of Neural Computation. Reading, MA: Addison-Wesley, 1991.
3. P. D. Wasserman, Neural Computing Theory and Practice. New York: Van Nostrand Reinhold, 1989.
4. J. J. Mulawka and B. K. Verma, "Improving the training time of the backpropagation algorithm," Int. J. Microcomputer Applicat., Canada, vol. 13, no. 2, pp. 85-89, 1994.
5. Y. Liping and Y. Wanzhen, "Backpropagation with homotopy," Neural Computa., vol. 5, no. 3, pp. 363-366, 1993.
6. B. K. Verma and J. J. Mulawka, "A modified backpropagation algorithm," in Proc. World Congr. Computa. Intell., Orlando, FL, vol. 2, June 26-July 2, 1994, pp. 840-846.
7. Y. Yam and T. Chow, "Extended backpropagation algorithm," Electron. Lett., vol. 29, no. 19, pp. 1701-1702, 1993.
8. J. A. Freeman, BP and Its Variants. Reading, MA: Addison-Wesley, 1994.
9. M. Fukumi and S. Omatu, "A new backpropagation algorithm with coupled neuron," IEEE Trans. Neural Networks, vol. 2, pp. 535-538, 1991.
10. F. Biegler and F. Barmann, "A learning algorithm for multilayer neural networks basded on linear least squares problems," Neural Networks, vol. 6, pp. 127-131, 1993.
11. B. K. Verma and J. J. Mulawka, "Training of the multilayer perceptron using direct solution methods," in Proc. 12th IASTED Int. Conf. Appl. Inform., Annecy, France, May 17-20, 1994, pp. 18-24.
12. B. K. Verma, "New methods of training the MLP," Ph.D. dissertation, Warsaw Univ. Technol., Poland, 1994.
13. S. D. Hunt and J. R. Deller, "Efficient training of feedforward artificial neural networks based on matrix perturbation theory," submitted to Int. J. Neural Syst., London, U.K.
14. A. Neubauer, "Robust learning algorithms for multilayer perceptrons with discretized synaptic weights," in Proc. IEEE Int. Conf. Neural Networks, ICNN'95, Australia, 1995, p. 3154.
15. M. H. Brugge and J. A. Nijhuis, "On the representation of data for optimal learning," in Proc. IEEE Int. Conf. Neural Networks, ICNN'95, Australia, 1995, p. 3180.
16. M. R. Azmi and R. Liou, "Fast learning process of MLP NN's using recursive least squares methods," IEEE Trans. Signal Processing, vol. 4, pp. 446-450, 1993.
17. A. Kielbasinski and H. Schwetlick, Numerical Linear Algebra, Warsaw, Poland: Polish Scientific Press, 1992.
18. A. Bjorck and G. Dahlquist, Numerical Methods. Englewood Cliffs, NJ: Prentice-Hall, 1974.
19. R. P. Gorman and T. J. Sejnowski, "Analysis of hidden units in a layered network trained to classify sonar targets," Neural Networks, vol. 1, pp. 75-89, 1988.
20. A. J. Robinson and F. Fallside, "Static and dynamic error propagation networks with applications to speech coding," Neural Inform. Processing Syst., pp. 632-641, 1988.