An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method
ABSTRACT
It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s data center based on the improvement density method. First it uses the improved density method to select RBFNN’s data center, and calculates the expansion constant of each center, then only trains the network weight with the gradient descent method. To compare this method with full-supervised gradient descent method, the time not only has obvious reduction (including to choose data center’s time by density method), but also obtains better classification results when using the data set in UCI to carry on the test to the network.
It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s data center based on the improvement density method. First it uses the improved density method to select RBFNN’s data center, and calculates the expansion constant of each center, then only trains the network weight with the gradient descent method. To compare this method with full-supervised gradient descent method, the time not only has obvious reduction (including to choose data center’s time by density method), but also obtains better classification results when using the data set in UCI to carry on the test to the network.
Cite this paper
M. Zheng and Y. Zhang, "An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method," Open Journal of Applied Sciences, Vol. 4 No. 1, 2014, pp. 1-5. doi: 10.4236/ojapps.2014.41001.
M. Zheng and Y. Zhang, "An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method," Open Journal of Applied Sciences, Vol. 4 No. 1, 2014, pp. 1-5. doi: 10.4236/ojapps.2014.41001.
References
[1] Chen, S., Cowan, C.F. and Grant, P.M. (1991) Orthogonal least squares learning algorithms for radial basis function networks. IEEE Transactions on Neural Networks, 2, 302-309. http://dx.doi.org/10.1109/72.80341 [2] Gomm, J.B. and Yu, D.L. (2000) Selecting radial basis function network centers with recursive orthogonal least squares training. IEEE Transactions on Neural Networks, 11, 306-314. http://dx.doi.org/10.1109/72.839002 [3] Han, M. and Guo, W. (2004) The full-supervised learning algorithm of radial basis function neural network. Chinese Journal of Science Instrument, 25, 454-457. [4] Toh, K.-A. and Mao, K.Z. (2002) A global transformation approach to RBF Neural Network learning. Proceedings of 16th International Conference on Pattern Recognition (ICPR’02), 2, 96-99. [5] Wei, H.K. and Ding, W.M. (2002) Dynamic method for designing RBF neural networks. Control Theory & Application, 19, 673-680. [6] Wettschereck, D. and Dietterich, T. (1992) Improving the performance of radial basis function networks by learning center locations. Moody, J., Hanson, S. and Lippman R., Eds., Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, San Mateo. [7] Tarassenko, L. and Roberts, S. (1994) Supervised and unsupervised learning in radial basis function classifiers. IEE Proceedings of Vision, Image and Signal Processing, 141, 210-216. http://dx.doi.org/10.1049/ip-vis:19941324 [8] Han, L.Q. (2006) Artificial neural network tutorial. Beijing University of Posts and Telecommunications Press, Beijing, 12Ver 1, 137. [9] Web Site of UCI Machine Learning Repository. (2002) University of California, Irvine. http://www.1.ics.uci.edu/~mlearn/MLRepository.html
[1] Chen, S., Cowan, C.F. and Grant, P.M. (1991) Orthogonal least squares learning algorithms for radial basis function networks. IEEE Transactions on Neural Networks, 2, 302-309. http://dx.doi.org/10.1109/72.80341 [2] Gomm, J.B. and Yu, D.L. (2000) Selecting radial basis function network centers with recursive orthogonal least squares training. IEEE Transactions on Neural Networks, 11, 306-314. http://dx.doi.org/10.1109/72.839002 [3] Han, M. and Guo, W. (2004) The full-supervised learning algorithm of radial basis function neural network. Chinese Journal of Science Instrument, 25, 454-457. [4] Toh, K.-A. and Mao, K.Z. (2002) A global transformation approach to RBF Neural Network learning. Proceedings of 16th International Conference on Pattern Recognition (ICPR’02), 2, 96-99. [5] Wei, H.K. and Ding, W.M. (2002) Dynamic method for designing RBF neural networks. Control Theory & Application, 19, 673-680. [6] Wettschereck, D. and Dietterich, T. (1992) Improving the performance of radial basis function networks by learning center locations. Moody, J., Hanson, S. and Lippman R., Eds., Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, San Mateo. [7] Tarassenko, L. and Roberts, S. (1994) Supervised and unsupervised learning in radial basis function classifiers. IEE Proceedings of Vision, Image and Signal Processing, 141, 210-216. http://dx.doi.org/10.1049/ip-vis:19941324 [8] Han, L.Q. (2006) Artificial neural network tutorial. Beijing University of Posts and Telecommunications Press, Beijing, 12Ver 1, 137. [9] Web Site of UCI Machine Learning Repository. (2002) University of California, Irvine. http://www.1.ics.uci.edu/~mlearn/MLRepository.html