Generalized Dai-Yuan conjugate gradient algorithm for training multi-layer feed-forward neural networks
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Abstract
In this paper, we will present different type of CG algorithms depending on Peary conjugacy condition. The new conjugate gradient training (GDY) algorithm using to train MFNNs and prove it's descent property and global convergence for it and then we tested the behavior of this algorithm in the training of artificial neural networks and compared it with known algorithms in this field through two types of issues.
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References
[1] Bishop, C. and Bishop, C. M. (1995). Neural
networks for pattern recognition. Oxford university
press.
[2] Rumelhart, D. E.; Hinton, G. E., and Williams, R.
J. (1985). Learning internal representations by error
propagation (No. ICS-8506). California Univ San
Diego La Jolla Inst for Cognitive Science.
[3] Hertz, J.; Krogh, A., and Palmer, R. G.
(1991). Introduction to the theory of neural
computation. Addison-Wesley/Addison Wesley
Longman.
[4] Nawi, N. M., Ransing, M. R., & Ransing, R. S.
(2006). An Improved Learning Algorithm based on
the Conjugate Gradient Method for Back Propagation
Neural Networks. Proc of World Academy of Science,
Eng, and Technology, 14.
[5] Beigi, H. S. and Li, C. J. (1993). Learning
algorithms for neural networks based on quasi-
Newton methods with self-scaling. Journal of
dynamic systems, measurement, and control, 115(1):
38-43.
[6] Fletcher, R., and Reeves, C. M. (1964). Function
minimization by conjugate gradients. The computer
journal, 7(2): 149-154.
[7] Poliak, B.T. (1969) .The Conjugate Gradient
Method in Extreme Problems, URSS Comp. Math.
Math. phys., 9(4): 94-112.
[8] Dai, Y. H., & Yuan, Y. (1999). A nonlinear
conjugate gradient method with a strong global
convergence property. SIAM Journal on optimization, 10 (1): 177-182. [9] Hestenes, M. R., and Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6): 409-436.
[10] Perry, A. (1978). A modified conjugate gradient algorithm, Operation Research, 26(6): 1073-1078. [11] Dai, Y. H. and Liao, L. Z. (2001). New conjugacy conditions and related nonlinear conjugate gradient methods. Applied Mathematics and Optimization, 43(1): 87-101.
[12] Nguyen, D., and Widrow, B. (1990, June). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 21-26). IEEE.