A diffusion-neural-network for learning from small samples

International Journal of Approximate Reasoning

Volumn 35, Issue 2, 2004, Pages 137-161

  Huang, Chongfu ^a   Moraga, Claudio ^b  

^a BEIJING NORMAL UNIVERSITY   (China)

^b TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Fuzzy information; Information diffusion; Neural network; Non linear function

Indexed keywords

COMPUTER SIMULATION; DIFFUSION; ERROR ANALYSIS; FUZZY SETS; INFORMATION ANALYSIS; NONLINEAR SYSTEMS;

DIFFUSION-NEURAL-NETWORKS (DNN); FUZZY INFORMATION; INFORMATION DIFFUSION; NON-LINEAR FUNCTIONS;

NEURAL NETWORKS;

EID: 0348230519     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2003.06.001     Document Type: Article

References (29)

1. Amirakian B., Nishimura H. What size network is good for generalization of a specific task of interest? Neural Networks. 7(2):1994;321-329.
2. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems. 2(4):1989;303-314.
3. L. Fletcher, V. Katkovnik, F.E. Steffens, Optimizing the number of hidden nodes of a feedforward artificial neural network, in: Proceedings of the International Joint Conference on Neural Networks, Anchorage, USA, 1998, pp. 1608-1612.
4. Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks. 2(3):1989;183-192.
5. Haykin S. Neural Networks: A Comprehensive Foundation. 1994;Prentice-Hall, Englewood Cliffs.
6. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks. 2(5):1989;359-366.
7. C.F. Huang, The principle of information diffusion and thought computation and their applications in earthquake engineering, Ph.D. Thesis, Beijing Normal University, Beijing, 1993 (in Chinese).
8. Huang C.F. Principle of information diffusion. Fuzzy Sets and Systems. 91(1):1997;69-90.
9. C.F. Huang, Deriving samples from incomplete data, in: Proceedings of FUZZ-IEEE'98, Anchorage, USA, 1998, pp. 645-650.
10. Huang C.F. Information diffusion techniques and small sample problem. International Journal of Information Technology and Decision Making. 1(2):2002;229-249.
11. Huang S.C., Huang Y.F. Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Transactions of Neural Network. 2(1):1991;47-55.
12. Huang C.F., Lueng Y. Estimating the relationship between isoseismal area and earthquake magnitude by hybrid fuzzy-neural-network method. Fuzzy Sets and Systems. 107(2):1999;131-146.
13. Huang C.F., Ruan D. Information diffusion principle and application in fuzzy neuron. Ruan D. Fuzzy Logic Foundations and Industrial Applications. 1996;165-189 Kluwer Academic Publishers, Boston.
14. Huang C.F., Shi Y. Towards Efficient Fuzzy Information Processing - Using the Principle of Information Diffusion. 2002;Physica-Verlag, Heidelberg.
15. P. Kulczycki, Estimating conditional distributions by neural networks, in: Proceedings of the International Joint Conference on Neural Networks, Anchorage, USA, 1998, pp. 344-1349.
16. K. Van Ha, Hierarchical radial basis function networks, in: Proceedings of the International Joint Conference on Neural Networks, Anchorage, USA, 1998, pp. 1893-1898.
17. Lu S., Basar T. Robust nonlinear system identification using neural network models. IEEE Transactions of Neural Network. 9(3):1998;407-429.
18. Moraga C. Neuro-fuzzy modeling of compensating systems. Sincák P., Vascák J. Quo Vadis Computational Intelligence? 2000;385-398 Physica-Verlag, Heidelberg.
19. Pao Y.H. Adaptive Pattern Recognition and Neural Networks. 1989;Addison-Wesley, Reading, MA.
20. Patrick K.S. Artificial Neural Systems Foundations, Paradigms, Applications, and Implementations. 1990;McGraw-Hill, New York.
21. Ripley B.D. Pattern Recognition and Neural Networks. 1996;Cambridge University Press, Cambridge.
22. Rumelhart D.E., McClelland J.L. the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. vols. 1&2:1986;MIT Press, Cambridge, MA.
23. Silverman B.W. Density Estimation for Statistics and Data Analysis. 1986;Chapman and Hall, London.
24. van der Smagt P.P. Minimisation methods for training feedforward networks. Neural Networks. 7(1):1994;1-11.
25. Wang P.Z. Fuzzy Sets and Falling Shadows of Random Sets. 1985;Beijing Normal University Press, Beijing. (in Chinese).
26. Wang Z.N., Dimassimo C., Tham M.T., Morris A.J. A procedure for determining the topology of multilayer feedforward neural networks. Neural Networks. 7(2):1994;291-300.
27. White H. Connectionist nonparametric regression: multilayer feedforward network can learn arbitrary mappings. Neural Networks. 3(5):1990;535-549.
28. Wray J., Green G.G.R. Neural networks, approximation theory, and finite precision computation. Neural Networks. 8(1):1995;31-37.
29. Zadeh L.A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems. 1(1):1978;3-28.