A New Training Algorithm for RBF Neural Network based on Dynamic Fuzzy Clustering

Yan Jun Cui; Yan Dong Ma; Jie Li; Zheng Zhao

doi:10.4028/www.scientific.net/AMM.241-244.1593

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 241-244A New Training Algorithm for RBF Neural Network...

A New Training Algorithm for RBF Neural Network based on Dynamic Fuzzy Clustering

Abstract:

A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper. This algorithm is based on the dynamic fuzzy clustering method (DFCM). The algorithm has a number of advantages compared to the traditional method based on k-means. For example, it does not need to know the number of the hidden nodes and to predicts more accurately. Due to these advantages, this method proves to be suitable for developing models for complex nonlinear systems.

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Periodical:

Applied Mechanics and Materials (Volumes 241-244)

Pages:

1593-1597

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.241-244.1593

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Online since:

December 2012

Authors:

Yan Jun Cui, Yan Dong Ma, Jie Li, Zheng Zhao

Keywords:

Dynamic Fuzzy Cluster Method (DFCM), Radial Basis Function Neural Network (RBFNN), Two-Phase Method

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References

[1] F. Schwenker, H.A. Kestler and G. Palm. Three learning phases for radial-basis-function networks. Neural Networks， 2001， 14(4): 439-458.

DOI: 10.1016/s0893-6080(01)00027-2

Google Scholar

[2] Luo Chengzhong, Introduction to Fuzzy Set, P105-114.

Google Scholar

[3] Girosi, F., Jones, M., & Poggi, T. (1995). Regularization Neural Computation, 7, 219-269.

Google Scholar

[4] Darken, C. & Moody, S. (1990). Fast adaptive k-means clustering: some empirial results. Proceedings International Joint Conference on Neural Networks. Piscataway: IEEE Press, 238.

DOI: 10.1109/ijcnn.1990.137720

Google Scholar

[5] Kohonen, T. (1995). Self-organizing maps, Berlin: Springer.

Google Scholar

[6] Hertz, J. Krogh, A., &Palmer, R. G. (1991). Introduction to the theory of neural computation, New York: Addison Wesley.

Google Scholar

[7] S. Nascimento, B. Mirkin and F. Moum-Pires, A Fuzzy Custering Model of Data and Fuzzy C-Means, 0-7803-5877-5100/$ 10. 00 OC 2000 IEEE.

Google Scholar

[8] D.S. Broomhead and D. Lowe. Multivariate functional interpolation and adaptive networks. Complex System， 1988， 2: 321-355.

Google Scholar