Abstract
For the reason of all the centers and radii needed to be adjusted iteratively, the learning speed of radial basis function (RBF) neural networks is always far slower than required, which obviously forms a bottleneck in many applications. To overcome such problem, we propose a fast and accurate RBF neural network in this paper. First we prove the universal approximation theorem for RBF neural networks with arbitrary centers and radii. Based on this theory, we propose a new learning algorithm called fast and accurate RBF neural network with random kernels (RBF-RK). With the arbitrary centers and radii, our RBF-RK algorithm only needs to adjust the output weights. The experimental results, on function approximation and classification problems, show that the new algorithm not only runs much faster than traditional learning algorithms, but also produces better or at least comparable generalization performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Poggio, T., Girosi, F.: Network for Approximation and Learning. Proc. IEEE 78, 1481–1497 (1992)
Wei, L.Y., Sundararajan, N., Saratchandran, P.: Performance Evaluation of a Sequential Minimal Radial Basis Function (RBF) Neural Network Learning Algorithm. IEEE Trans. Neural Networks 9, 308–318 (1998)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
Uykan, Z., Guzelis, C., Celebi, M.E., Koivo, H.N.: Analysis of Input-output Clustering for Determining Centers of RBFN. IEEE Trans. Neural Networks 11, 851–858 (2000)
Gomm, J.B., Yu, D.L.: Selecting Radial Basis Function Network Centers with Recursive Orthogonal Least Squares Training. IEEE Trans. Neural Networks 11, 306–314 (2000)
Schokopf, R., Sung, K.K., Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.N.: Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers. IEEE Trans. Signal Processing 45, 2758–2765 (1997)
Lowe, D.: Adaptive Radial Basis Function Nonlinearities, and the Problem of Generalisation. In: First IEEE International Conference on Artificial Neural Networks, pp. 29–33 (1989)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine. In: International Joint Conference on Neural Networks, IJCNN 2004 (2004)
Liu, B., Wan, C.R., Wang, L.: A Fast and Accurate RBF Neural Network with Random Kernels (submitted for publication)
Haykin, S.: Neural Networks-a Comprehensive Foundation, 2nd edn. Prentice Hall International, Inc., Englewood Cliffs (1999)
Golub, G.H., Loan, C.F.V.: Matrix Computation, 3rd edn. Johns Hopkins University Press, Baltimore (1996)
Blake, C., Merz, C.: UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/~mlearn/MLRepository.html , Department of Information and Computer Science, University of California, Irvine, USA (1998)
Ratsch, G., Onoda, T., Muller, K.R.: An Improvement of AdaBoost to Avoid Overfitting. In: Proceedings of the 5th International Conference on Neural Information Processing, ICONIP 1998 (1998)
Romero, E.: Function Approximation with Saocif: a General Sequential Method and a Particular Algorithm with Feed-forward Neural Networks. Department de Llenguatgesi Sistemes Informatics, Universitat Politecnica de catalunya (2001), http://www.lsi.upc.es/dept/techreps/html/R01-41.html
Romero, E.: A New Incremental Method for Function Approximation Using Feedforward Neural Networks. In: Proc. INNS-IEEE International Joint Conference on Neural Networks (IJCNN 2002), pp. 1968–1973 (2002)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Wilson, D.R., Martinez, T.R.: Heterogeneous Radial Basis Function Networks. In: Proceedings of the International Conference on Neural networks (ICNN 1996), pp. 1263–1267 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Liu, B., Wan, C. (2004). A Novel RBF Neural Network with Fast Training and Accurate Generalization. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-30497-5_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24127-0
Online ISBN: 978-3-540-30497-5
eBook Packages: Computer ScienceComputer Science (R0)