Predicting sun spots using a layered perceptron neural network

IEEE Transactions on Neural Networks

Volumn 7, Issue 2, 1996, Pages 501-505

  Park, Young R ^a   Murray, Thomas J ^b   Chen, Chung ^c  

^a University System of Georgia   (United States)

^b TEXAS A AND M INTERNATIONAL UNIVERSITY   (United States)

^c SYRACUSE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; LEARNING SYSTEMS; OPTIMAL SYSTEMS; PARAMETER ESTIMATION; PERFORMANCE; SYSTEMS ANALYSIS;

LAYERED PERCEPTRON NEURAL NETWORK; PRINCIPAL COMPONENT ANALYSIS; SUN SPOTS; TIME SERIES FORECASTING PROBLEM;

FEEDFORWARD NEURAL NETWORKS;

EID: 0030109788     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.485683     Document Type: Article

References (37)

1. H. Akaike, "A new look at the statistical model identification," IEEE Trans. Automat. Contr., vol. AC-19, no. 6, pp. 716-723, Dec. 1974.
2. L. Almeida and F. M. Silva, "Adaptive decorrelation," in Artificial Neural Networks, vol. 2, I. Aleksander and J. Taylor, Eds. Amsterdam, The Netherlands: Elsevier, 1992, pp. 149-156.
3. G. E. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 1970.
4. R. P. Brent, "Fast training algorithms for multilayer neural nets," IEEE Trans. Neural Networks, vol. 2, no. 3, pp. 346-354, May 1991.
5. Y. Chauvin, "A backpropagation algorithm with optimal use of hidden units," in Advances in Neural Information Processing Systems, vol. 1, D. Touretzky and S. David, Eds. San Mateo, CA: Morgan Kaufmann, 1989. pp. 519-526.
6. S. E. Fahlman and C. Lebiere, "The cascade-correlation learning architecture," in Advances in Neural Information Processing Systems, vol. 2, D. Touretzky and S. David, Eds. San Mateo, CA: Morgan Kaufmann, 1990, pp. 524-532.
7. D. B. Fogel, "An information criterion for optimal neural network selection," IEEE Trans. Neural Networks, vol. 2, no. 5, pp. 490-497, Sept. 1991.
8. M. Frean, "The upstart algorithm: A method for constructing and training feedforward neural networks," Neural Computa., vol. 2, pp. 198-209, 1990.
9. S. I. Gallant, "Perceptron-based learning algorithms," IEEE Trans. Neural Networks, vol. 1, no. 2, pp. 179-191, June 1990.
10. M. Gutierrez, J. Wang, and R. Grondin, "Estimating hidden unit number for two-layer perceptrons," in Proc. IEEE Int. Conf. Neural Networks, vol. 1, 1988, pp. 677-681.
11. S. J. Hanson and L. Y. Pratt, "Comparing biases for minimal network construction with backpropagation," in Advances in Neural Information Processing Systems, vol. 1, D. Touretzky and S. David, Eds. San Mateo, CA: Morgan Kaufmann, 1989, pp. 177-185.
12. Y. Hirose, K. Yamashita, and S. Hijiya, "Backpropagation algorithm which varies the number of hidden units," Neural Networks, vol. 4, no. 1, pp. 61-65, 1991.
13. C. Ho, "On multilayered connectionist models: adding layers vs. increasing width," in Proc. 11th Int. Joint Conf. Artificial Intell., Aug. 1989, vol. 1, pp. 176-179.
14. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," in Proc. Nat. Academy Sci., vol. 79, no. 8, Apr. 1982, pp. 2554-2558.
15. K. Hornik, M. Stinchcombe, and H. White, "Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks," Neural Networks, vol. 3, no. 5, pp. 551-560, 1990.
16. K. Hornik, "Approximation capabilities of multilayer feedforward networks," Neural Networks, vol. 4, no. 2, pp. 251-257, 1991.
17. M. I. Jordan, "Supervised learning and systems with excess degrees of freedom," MIT Dep. Cognition Brain Sci., Tech. Rep. COINS-88-27, 1988.
18. K. Knight, "A gentle introduction to subsymbolic computation: Connectionism for the A.I. researcher," Carnegie Mellon Univ. Working Paper, CMU-CS-89-150, May 1989.
19. T. Kohonen, Self-Organization and Associative Memory. Berlin: Springer-Verlag, 1984.
20. S. Y. Kung and J. N. Hwang, "An algebraic projection analysis for optimal hidden units size and learning rates in backpropagation learning," in Proc. Int. Joint Conf. Neural Networks, San Diego, CA, vol. 1, 1988, pp. 363-370.
21. E. Levin, "A recurrent neural network: Limitation and training," Neural Networks, vol. 3, no. 6, pp. 641-650, 1990.
22. R. P. Lippmann, "An introduction to computing with neural networks," IEEE Acoust., Speech, Signal Processing Mag., vol. 4, no. 2, pp. 4-22, Apr. 1987.
23. N. Morgan and H. Bourlard, "Generalization and parameter estimation in feedforward nets: Some experiments," in Advances in Neural Information Processing Systems, vol. 2, D. Touretzky and S. David, Eds. San Mateo, CA: Morgan Kaufmann, 1990, pp. 630-637.
24. D. F. Morrison, Multivariate Statistical Methods, 2nd ed. New York: McGraw-Hill, 1976.
25. E. Oja, "Neural networks, principal components and subspaces," Int. J. Neural Syst., vol. 1, no. 1, pp. 61-68, 1989.
26. M. D. Plumbley, "Lyapunov functions for convergence of principal components algorithms," Neural Networks, vol. 8, no. 1, pp. 11-23, 1995.
27. R. Reed, "Pruning algorithms - A survey," IEEE Trans. Neural Networks, vol. 4, no. 5, pp. 740-747, Sept. 1993.
28. F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington, D.C.: Spartan, 1962.
29. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. Cambridge, MA: MIT Press, 1986, no. 2.
30. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Eds. Cambridge, MA: MIT Press, 1986, pp. 319-362.
31. J. Sietsma and R. J. Dow, "Neural net pruning - Why and how," in Proc. Int. Joint Conf. Neural Networks, San Diego, CA, vol. 1, 1988 pp. 325-333.
32. _, "Creating artificial neural networks that generalize," Neural Networks, vol. 4, no. 1, pp. 67-79, 1991.
33. Z. Tang and P. A. Fishwick, "A comparative study of the neural network and Box-Jenkins methodology in forecasting modeling," Univ. Florida Working Paper, UF-CIS-TR-90-13, June 12, 1990.
34. A. S. Weigend, B. A. Huberman, and D. E. Rumelhart, "Predicting the future: A connectionist approach," Int. J. Neural Syst., vol. 1, no. 3, pp. 193-209, 1990.
35. A. S. Weigend, D. E. Rumelhart, and B. A. Huberman, "Generalization by weight-elimination applied to currency exchange rate prediction," in Proc. Int. Joint Conf. Neural Networks, Seattle, WA, vol. 1, 1991 pp. 837-841.
36. H. White, "An additional hidden unit test for neglected nonlinearity in multilayer feedforward networks," in Proc. Int. Joint Conf. Neural Networks, Washington, D.C., vol. 2, 1989 pp. 451-455.
37. R. J. Williams and D. Zipper, "A learning algorithm for continually running fully recurrent neural networks," Neural Computa., vol. 1, pp. 270-280, 1989.