Training Radial Basis Function Networks with Differential Evolution

Authors: Bing Yu , Xingshi He

Abstract:

In this paper, Differential Evolution (DE) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, New-thyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, DE achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.

Keywords: differential evolution, neural network, Rbf function

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332416

References:

[1] Matysiak, Broomhead, D., Lowe, D.: Multivariable Functional Interpolation and Adaptive Networks. Complex Systems (1988) 321-355.
[2] Moody, J., Darken, C.: Fast Learning Networks of Locally-Tuned Processing Units. Neural Computation (1991) 579-588.
[3] Vogt, M.: Combination of Radial Basis Function Neural Networks with Optimized Learning Vector Quantization. IEEE International Conference on Neural Networks (1993) 1841-1846.
[4] Kubat, M.: Decision Trees Can Initialize Radial-Basis Function Networks. IEEE Transactions on Neural Networks (1998) 813-821.
[5] Robert, J., Hewlett L.C.J.: Radial Basis Function Networks 2: New Advances in Design (2001).
[6] Yao, X.: Evolving Artificial Neural Networks. Proceedings of the IEEE (1999) 87(9) 1423-1447.
[7] Rainer Storn, Kenneth Price: Differential Evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. Global Optimization, 11, 1997 341-359.
[8] PATERLINI, S., AND KRINK, T. High performance clustering using differential evolution. In Proceedings of the Six Congress on Evolutionary Computation (CEC-2004), IEEE Press, Piscataway, NJ, USA.
[9] THOMSEN, R. Flexible ligand docking using differential evolution. In Proceedings of the Fifth Congress on Evolutionary Computation (CEC-2003) (2003), vol. 4, IEEE Press, Piscataway, NJ, USA, pp. 2354-2361.
[10] URSEM, R. K., AND VADSTRUP, P. Parameter identification of induction motors using differential evolution. In Proceedings of the Fifth Congress on Evolutionary Computation (CEC-2003) (2003), IEEE Press, Piscataway, NJ, USA, pp. 790-796.
[11] Blake, C.,Keogh,E.,Merz,C.J.:UCI Repository of Machine Learning Databases(1998)www.ics.uci.edu/mlearn/MLRepository.html