An incremental learning algorithm for function approximation

Advances in Engineering Software

Volumn 40, Issue 8, 2009, Pages 725-730

  Bahi, Jacques M ^a   Contassot Vivier, Sylvain ^b   Sauget, Marc ^a  

^a UNIVERSITÉ DE FRANCHE COMTÉ   (France)

^b LORIA   (France)

Author keywords

Function approximation; Incremental learning; Neural networks

Indexed keywords

APPROXIMATION ALGORITHMS; EDUCATION; FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS;

APPROXIMATORS; CASCADE CORRELATIONS; FUNCTION APPROXIMATION; INCREMENTAL ALGORITHMS; INCREMENTAL LEARNING; RADIATION DOSE; REAL-WORLD DATUM; SYNTHETIC DATUM;

LEARNING ALGORITHMS;

EID: 64849110433     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2008.12.018     Document Type: Article

References (14)

1. Bahi J., Contassot-Vivier S., Makovicka L., Martin E., and Sauget M. Neural network based algorithm for radiation dose evaluation in heterogeneous environments. Artificial neural networks-ICANN 2006, Athens, Greece. Lecture notes in computer science vol. 4132/2006 (2006), Springer, Berlin/Heidelberg 777-787
2. Cybenko G. Approximations by superpositions of sigmoidal functions. Math Control Signal 2 (1989) 303-314
3. Dunkin N., Shawe-Taylor J., and Koiran P. A new incremental learning technique. Neural Nets Wirn Vietri-96. Proceedings of the eighth Italian workshop on neural nets (1997), Springer Verlag 112-118
4. Fahlman S.E., and Lebiere C. The cascade-correlation learning architecture. In: Touretzky D.S. (Ed). Advances in neural information processing systems, Denver 1989 vol. 2 (1990), Morgan Kaufmann, San Mateo 524-532
5. Fritzke B. Fast learning with incremental RBF networks. Neural Process Lett 1 1 (1994) 2-5
6. Ghosh J., and Shin Y. Efficient higher-order neural networks for classification and function approximation. Int J Neural Syst 3 4 (1992) 323-350
7. Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks are universal approximators. Neural Networks 2 5 (1989) 359-366
8. Kurkova V, Beliczynski B. An incremental learning algorithm for gaussian radial-basis-function approximation. In: Second international symposium on methods and models in automation and robotics; 1995. p. 675-80.
9. Le Cun Y. Modèles connexionnistes de l'apprentissage. PhD thesis, Université Pierre et Marie Curie; 1987.
10. Pitts W., and McCulloch W.S. A logical calculus of ideas immanent in nervous activity. Bull Math Biophys 5 (1943) 115-133
11. Polikar R., Udpa L., Udpa S.S., and Honavar V. Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cyber Appl Rev 31 4 (2001) 497-508
12. Riedmiller M, Braun H. A direct adaptative method for faster backpropagation learning: The RPROP algorithm. In: Proceedings of the IEEE international conference on neural networks (ICNN93), San Francisco, April 1993.
13. Rumelhart D.E., McClelland J.L., and the PDP research group. Parallel distributed processing vol. 1-2 (1986-87), The MIT Press
14. Tertois S. Réduction des effets des non-linéarités dans une modulation miltiporteuse á l'aide de réseaux de neurones. PhD thesis, Rennes 1; 2003.