Subscribe Now Subscribe Today
Research Article
 

Comparision of Neural Algorithms for Funchtion Approximation



Lale Ozyilmaz , Tulay Yildirim and Kevser Koklu
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail
ABSTRACT

In this work, various neural network algorithms have been compared for function approximation problems. Multilayer Perceptron (MLP) structure with standard back propagation, MLP with fast back propagation (adaptive learning and momentum term added), MLP with Levenberg-Marquardt learning algorithms, Radial Basis Function (RBF) network structure trained by OLS algorithm and Conic Section Function Neural Network (CSFNN) with adaptive learning have been investigated for various functions. Results showed that the neural algorithms can be used for functional estimation as an alternative to classical methods.

Services
Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

 
  How to cite this article:

Lale Ozyilmaz , Tulay Yildirim and Kevser Koklu , 2002. Comparision of Neural Algorithms for Funchtion Approximation. Journal of Applied Sciences, 2: 288-294.

DOI: 10.3923/jas.2002.288.294

URL: https://scialert.net/abstract/?doi=jas.2002.288.294

REFERENCES
1:  Sherif, M.B. and C.G. Atkeson, 1991. Generalization properties of radial basis functions. Proceedings of the Conference on Advances in Neural Information Processing Systems 3, (NIPS`91), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA., pp: 707-713.

2:  Broomhead, D.S. and D. Lowe, 1988. Multivariable functional interpolation and adaptive networks. Complex Syst., 2: 321-355.
Direct Link  |  

3:  Dorffner, G., 1994. Unified framework for mlps and rbfns: Introducing conic section function networks. Cybernetics Syst., 25: 511-554.
CrossRef  |  Direct Link  |  

4:  Geva, S. and J. Sitte, 1992. A constructive method for multivariate function approximation bymultilayer perceptrons. IEEE Trans. Neural Networks, 3: 621-624.
CrossRef  |  Direct Link  |  

5:  Hagan, M.T., H.B. Demuth and M.H. Beale, 1996. Neural Network Design. 1st Edn., PWS Publishing Co., Boston, MA, USA., ISBN: 0-53494332-2.

6:  Haykin, S., 1994. Neural Networks: A Comprehensive Foundation. Macmillian College Publishing Company, New York, ISBN-10: 0023527617.

7:  Hush, D.R. and B.G. Horne, 1993. Progress in supervised neural networks. IEEE Signal Process. Magazine, 10: 8-39.
CrossRef  |  Direct Link  |  

8:  Jondarr, G.H., 1996. Backpropagation family album. Technical Report C/TR96-05, Department of Computing, Macquarie University, New South Wales.

9:  Krose, B.J.A. and P.P. van der Smagt, 1993. An Introduction to Neural Networks. 5th Edn., University of Amsterdam, The Netherlands.

10:  Lippmann, R., 1987. An introduction to computing with neural nets. IEEE ASSP Mag., 4: 4-22.
CrossRef  |  Direct Link  |  

11:  Izyilmaz, L. and T. Yildirim, 2000. Using conic section function neural network for function approximation. Proceedings of the 9th Turkish Symposium of Artificial Intelligence and Neural Networks, June 21-23, Izmir, Turkey, pp: 21-23.

12:  Poggio, T. and F. Girosi, 1990. Networks for approximation and learning. Proc. IEEE, 78: 1481-1497.
CrossRef  |  Direct Link  |  

13:  Riedmiller, M. and H. Braun, 1997. RPROP-A fast adaptive learning algorithm. Proceedings of the ISCIS VII.

14:  Rumelhart, D.E., G.E. Hinton and R.J. Williams, 1987. Learning Internal Representations by Error Propagation. In: Parallel Distributing Processing, Rumelhart, D.E., G.E. Hinton and R.J. Williams (Eds.). MIT Press, Cambridge, MA, pp: 318-362.

15:  Vysniauskas, V., F.C.A. Groen and B.J.A. Krose, 1993. The optimal number of learning samples and hidden units in function approximation with a feedwork network. Technical Report CS-93-15, University of Amsterdam, The Netherlands.

16:  Yildirim, T. and J.S. ve Marsland, 1997. A Unified Framework for Connectionist Models. In: Perspectives in Neural Computing: Connectionist Representations, Bullinaria, J.A., D.W. Glasspool and G. Houghton (Eds.). Springer, London, UK.

17:  Zurada, J.M., 1995. Introduction to Artificial Neural Systems. PWS Publishing, Boston, MA, USA.

18:  Chen, S., C.F.N. Cowan and P.M. Grant, 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks, 2: 302-309.
CrossRef  |  Direct Link  |  

©  2021 Science Alert. All Rights Reserved