Extrapolation detection and novelty-based node insertion for sequential growing multi-experts network

Neural Network World

Volumn 13, Issue 2, 2003, Pages 151-176

  Kiong, Loo Chu ^a,b   Rajeswari, Mandava ^a,b   Rao, M V C ^a  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

^b SCHOOL OF PHYSICS   (Malaysia)

Author keywords

Certainty factor; Extrapolation; Novelty based insertion; On line learning

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; EXTRAPOLATION; LEARNING SYSTEMS; MATHEMATICAL MODELS;

CERTAINTY FACTOR; GROWING MULTI-EXPERTS NETWORK; NOVELTY-BASED INSERTION; ON-LINE LEARNING;

NEURAL NETWORKS;

EID: 0038284255     PISSN: 12100552     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. Hornik K.: Multilayer feedforward networks are universal approximators, Neural Networks, 2, 5, 1989, pp. 359-366.
2. Park J., Sandberg I. W.: Universal approximation using radial basis function networks, Neural Computation, 5, 2, 1991, pp. 305-316.
3. Kiong C. L., Rajeswari M.: Redundant Expert Detection for a Growing Multi-Experts Network, Regional Symposium on Quality and Automation, Penang, Malaysia, May 2000.
4. Kiong C. L., Rajeswari M.: Growing Multi-Experts Network, TENCON 2000, Kuala Lumpur, Malaysia, Sept 2000.
5. Kiong, C. L., Rajewswari M.: Growing Multi-Experts Network for Industrial Control Applications, World Conference on Soft Computing in Industrial Applications, December 2000.
6. Fu K. S.: Neural Networks in Computer Intelligence. New York: McGrawhill, 1994.
7. Girosi F., Jones M., Poggio T.: Regularization Theory and Neural Networks Architectures, Neural Computation, 7, 2, 1995, pp. 219-269.
8. Moody, J., Darken C. J.: Fast learning in networks of locally tuned processing units, Neural Computation, 1, 1989, pp. 281-294.
9. Fritzke B.: Fast learning with incremental RBF networks, Neural Processing Letters, 1, 1, 1994, pp. 2-5.
10. Specht D. E.: A General Regression Neural Network, IEEE Transactions on Neural Networks, 2, 6, 1991, pp. 568-576.
11. Fritzke B.: Incremental Neuro-fuzzy Systems, In Proceedings of Applications of Soft Computing, SPIE International Symposium on Optical Science, Engineering and Instrumentation, San Diego, 27 July - 1 August 1997.
12. Takagi T., Sugeno M.: Derivation of fuzzy control rules from human operator's control actions, In Proc. IFAC Symp. Fuzzy Information, Knowledge Representation and Decision Analysis, July 1983, pp. 55-56.
13. Takagi T., Sugeno M.: Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on System, Man and Cybernetics, 15, 1, pp. 116-132.
14. Murray-Smith R.: Local model networks and local learning, In Fuzzy-Duisburg'94, University of Duisburg, April 7, 1994, pp. 404-4098.
15. Jacobs R. A., Jordan M. I., Nowlan S. J., Hinton G. E.: Adaptive Mixture of Local Experts, Neural Computation, 3, 1991, pp. 79-87.
16. Schaal S., Atkeson C. G.: From Isolation to Cooperation: An Alternative View of a System of Experts, Advances in Neural Information Processing Systems, 8, Cambridge, MA: MIT Press, 1996, pp. 605-611.
17. Werntges H. W.: Partitions of unity improve neural function approximation, In Proc. IEEE Int. Conf. Neural Networks, San Francisco, CA, 2, 1993, pp. 914-918.
18. Ljung L., Soderstrom T.: Theory and practice of recursive identification, Cambridge, MIT Press, 1983, pp. 57-59.
19. Donald K., Wedding II, Cios K. J.: Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model, Neurocomputing, 10, 1996, pp. 149-168.
20. Martinetz T. M.: Topology representing networks, Neural Networks, 7, 3, 1994, pp. 507-522.
21. Omohundro S. M.: The Delaunay triangulation and function learning, Tr-90-001, International Computer Science Institute, Berkeley, 1990.
22. Buchanan B. G., Shortliffe E. H.: Rule-based Expert Systems, Addison-Wesley, Reading, MA, 1985.
23. Chen S., Cowan C. F. N., Grant P. M.: Orthogonal least squares learning algorithm for radial basis function network, IEEE Transactions on Neural Networks, 2, 1991, pp. 302-309.
24. Cho, K. B., Wang B. H.: Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction, Fuzzy Sets and Systems, 83, 1996, pp. 325-339.
25. Lightbody G., Irwin, G. W.: Nonlinear Control Structure Based on Embedded Neural System Models, IEEE Transactions on Neural Networks, 8, 1997, pp. 553-567.