Self-Organizing Network for Optimum Supervised Learning

IEEE Transactions on Neural Networks

Volumn 1, Issue 1, 1990, Pages 100-110

  Tenorio, Manoel F ^a   Lee, Wei Tsih ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING--ALGORITHMS; MATHEMATICAL STATISTICS--TIME SERIES ANALYSIS; PATTERN RECOGNITION;

CHAOTIC TIME SERIES; PATTERN CLASSIFIERS; SELF-ORGANIZING NEURAL NETWORKS; SUPERVISED LEARNING; SYSTEM IDENTIFICATION;

SYSTEMS SCIENCE AND CYBERNETICS;

EID: 0025399568     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.80209     Document Type: Article

References (32)

1. A. Lapedes and R. Farber, “Nonlinear signal processing using neural networks; Prediction and system modeling,” TR LA-UR-87-2662, 1987.
2. J. L. Marroquin, “Probabilistic solution of inverse problems,” Ph.D. dissertation, M.I.T., Cambridge, MA, Sept. 1985.
3. B. Widrow and S. D. Steams, Adaptive Signal Processing. New Jersey: Prentice-Hall, 1985.
4. L. A. Zadeh, “From circuit to system theory,” Proc. IRE, vol. 50, pp. 856-865, 1962.
5. K. J. Åström and P. Eykhoff, “System identification—A survey,” Automatica, vol. 7, pp. 123-162, 1971.
6. D. Garbor et al., “A universal nonlinear filter, predictor and simulator which optimizes itself by a learning process,” Proc. Inst. Elec. Eng., vol. 108B, pp. 422-438, 1961.
7. R. J. Roy and J. Sherman, “A learning technique for Volterra series representation,” IEEE Trans. Automat. Contr., pp. 761-764, Dec. 1967.
8. A. G. Ivakhnenko, “Polynomial theory of complex systems,” IEEE Trans. Syst., Man, Cybern., vol. SMC-1, no. 4, pp. 364-378, Oct. 1971.
9. J. J. Duffy and M. A. Franklin, “A learning identification algorithm and its application to an environmental system,” IEEE Trans. Syst., Man, Cybern., vol. SMC-5, no. 2, pp. 226-240, 1975.
10. S. Ikeda, M. Ochiai, and Y. Sawarogi, “Sequential GMDH algorithm and its application to river flow prediction,” IEEE Trans. Syst., Man, Cybern., vol. SMC-6, no. 7, pp. 473-479, July 1976.
11. H. Tamura and T. Kondo, “Heuristics free group method of data handling algorithm of generating optimal partial polynomials with application to air pollution predication,” Int. J. Syst. Sci., vol. 11, no. 9, pp. 1095-1111, 1980.
12. J. Rissanen, “Modeling by shortest data description,” Automatica, vol. 14, pp. 465-471, 1978.
13. S. Geman and D. Geman, “Stochatic relaxation, Gibbs distribution, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Machine Intellig., vol. PAMI-6, pp. 721-741, 1984.
14. J. Moody, “Fast learning in multiresolution hierarchies,” in Advances in Neural Information Processing Systems I, D. S. Touretzky, Ed. 1989, pp. 29-39.
15. S. J. Hansen and Y. Pratt, “Comparing biases for minimal network construction with backpropagation,” in Advances in Neural Information Processing Systems I, D. S. Touretzky, Ed. 1989, pp. 177-185.
16. J. Rissanen, “Consistent order estimation of autoregression processes by shortest description of data,” in Analysis and Optimization of Stochastic System, Jacobs et al., Eds. New York: Academic, 1980.
17. J. Rissanen, “A universal prior for integers and estimation by minimum description length,” Annu. Statist., vol. 11, no. 2, pp. 416-431, 1983.
18. M. Feder, “Maximum entropy as a special case of the minimum description length criterion,” IEEE Trans. Inform. Theory, vol. IT-32, no. 6, pp. 847-849, Nov. 1986.
19. H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automat. Contr., vol. AC-19, no. 6, pp. 716-722, Dec. 1974.
20. H. Akaike, “Automatic data structure search by the maximum like-hood,” in Proc. Comput. Biomed. Suppl., 5th Hawaii Int. Conf. Syst., 1972, pp. 99-101.
21. R. L. Kashyap, “Inconsistency of AIC rule,” IEEE Trans. Automat. Contr., vol. AC-25, no. 5, pp. 997-998, 1980.
22. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671-680, May 1983.
23. S. B. Gelfand and S. K. Mitter, “Analysis of simulating annealing for optomization,” in Proc. 24th Conf. Decision and Contr., Dec. 1985, pp. 779-786.
24. J. D. Farmer and J. J. Sidorowich, “Predicting chaotic time series,” Phys. Rev. Lett., vol. 59, no. 8, pp. 845-848, 1987.
25. N. H. Packard, J. P. Crutchfield, J. D. Farmer, and R. S. Shaw, “Geometry from a time series,” Phys. Rev. Lett., vol. 45, no. 9, pp. 712-716, 1980.
26. M. Mackey and L. Glass, “Oscillation and chaos in physiological control system,” Science, pp. 197-287, 1977.
27. J. S. Albus, Brain, Behavior, and Robotics. Byte Books, 1981.
28. F. Takens, “Detecting strange attractor in turbulence,” in Lecture Notes in Mathematics, vol. 898, D. Rand, L. Young, Eds. Berlin, West Germany: Springer Verlag, 1981, p. 366.
29. K. Fukunaga, Introduction to Statistical Pattern Recognition. New York: Academic, 1978.
30. J. L. C. Sanz and E. B. Hinkle, “Note on contrast measure and polynomial classifiers,” in Proc. IEEE, vol. 76, no. 3, pp. 256-259, Mar. 1988.
31. A. Lapedes and R. Farber, “How neural networks work,” TR LA-UR-88-418, 1987.
32. M. F. M. Tenorio and W.-T. Lee, “Self-organizing neural network for the identification problem,” in Advances in Neural Information Processing Systems I, D. S. Touretzky, Ed. 1989, pp. 57-64.