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

Applied Soft Computing Journal

Volumn 3, Issue 2, 2003, Pages 159-175

  Kiong, Loo Chu ^a,b   Rajeswari, Mandava ^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; BENCHMARKING; CONTROL NONLINEARITIES; ERROR ANALYSIS; FUNCTIONS; MATHEMATICAL MODELS; NEURAL NETWORKS; ONLINE SYSTEMS;

CERTAINITY FACTOR; EMPIRICAL-BASED MODEL; NOVELTY-BASED INSERTION; ON-LINE LEARNING;

EXTRAPOLATION;

EID: 15944389474     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1568-4946(03)00011-5     Document Type: Article

References (25)

1. K. Hornik, Multilayer feedforward networks are universal approximators, Neural Netw. 2 (5) (1989) 359-366.
2. J. Park, I.W. Sandberg, Universal approximation using radial basis function networks, Neural Comput. 5 (2) (1991) 305-316.
3. L. Chu Kiong, M. Rajeswari, Redundant expert detection for a growing multi-experts network, in: Proceedings of the Regional Symposium on Quality and Automation, Penang, Malaysia, May 2000.
4. L. Chu Kiong, M. Rajeswari, Growing multi-experts network, in: Proceedings of the TENCON 2000, Kuala Lumpur, Malaysia, September 2000.
5. L. Chu Kiong, M. Rajeswari, Growing multi-experts network for industrial control applications, in: Proceedings of the World Conference on Soft Computing in Industrial Applications, Finland, December 2000.
6. K.S. Fu, Neural Networks in Computer Intelligence, McGraw Hill, New York, 1994.
7. F. Girosi, M. Jones, T. Poggio, Regularization theory and neural networks architectures, Neural Comput. 7 (2) (1995) 219-269.
8. J. Moody, C.J. Darken, Fast learning in networks of locally tuned processing units, Neural Comput. 1 (1989) 281-294.
9. B. Fritzke, Fast learning with incremental RBF networks. Neural Process. Lett. 1 (1) (1994) 2-5.
10. D.E. Specht, A general regression neural network, IEEE Trans. Neural Netw. 2 (6) (1991) 568-576.
11. B. Fritzke, 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. T. Takagi, M. Sugeno, Derivation of fuzzy control rules from human operator's control actions, in: Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, July 1983, pp. 55-56.
13. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on System, Man and Cybernetics, vol. 15 (1), 1985, pp. 116-132.
14. R. Murray-Smith, Local model networks and local learning, in: Fuzzy-Duisburg'94, University of Duisburg, 7 April 1994, pp. 404-409.
15. R.A. Jacobs, M.I. Jordan, S.J. Nowlan, G.E. Hinton, Adaptive mixture of local experts, Neural Comput. 3 (1991) 79-87.
16. S. Schaal, C.G. Atkeson, From Isolation to Cooperation: An Alternative View of a System of Experts, Advances in Neural Information Processing Systems 8, MIT Press, Cambridge, MA, 1996, pp. 605-611.
17. H.W. Werntges, Partitions of unity improve neural function approximation, in: Proceedings of the IEEE International Conference on Neural Networks, vol. 2, San Francisco, CA, 1993, pp. 914-918.
18. L. Ljung, T. Soderstrom, Theory and Practice of Recursive Identification, MIT Press, Cambridge, MA, 1983, pp. 57-59.
19. D.K. Wedding II, K.J. Cios, Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model, Neurocomputing 10 (1996) 149-168.
20. T.M. Martinetz, Topology representing networks, Neural Netw. 7 (3) (1994) 507-522.
21. S.M. Omohundro, The Delaunay Triangulation and Function Learning, Tr-90-001, International Computer Science Institute, Berkeley, 1990.
22. B.G. Buchanan, E.H. Shortliffe, Rule-Based Expert Systems, Addison-Wesley, Reading, MA, 1985.
23. S. Chen, C.F.N. Cowan, P.M. Grant, Orthogonal least squares learning algorithm for radial basis function network, IEEE Trans. Neural Netw. 2 (1991) 302-309.
24. K.B. Cho, B.H. Wang, Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction, Fuzzy Sets Syst. 83 (1996) 325-339.
25. G. Lightbody, G.W. Irwin, Nonlinear control structure based on embedded neural system models, IEEE Trans. Neural Netw. 8 (1997) 553-567.