Abstract
The method of determining the structures and parameters of radial basis function neural networks (RBFNNs) using improved genetic algorithms is proposed. Akaike’s information criterion (AIC) with generalization error term is used as the best criterion of optimizing the structures and parameters of networks. It is shown from the simulation results that the method not only improves the approximation and generalization capability of RBFNNs, but also obtain the optimal or suboptimal structures of networks.
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References
Powell M J D. Radial basis functions for multivariable interpolation: A review. In: Mason J C & Cox M G, eds. Algorithms for Approximation. Oxford: Clarendon Press, 1985. 143–167
Broomhead D S, Lowe D. Multivariable functional interpolation and adaptive networks. Complex Systems, 1988(2):321–355
Light W A. Some aspects of radial basis function approximation. Approximation Theory, Spline Functions and Applications, 1992,356:163–190
Powell M J D. The theory of radial basis function approximation in 1990. In: Light W A ed. Advances in Numerical Analysis. Oxford: Oxford University Press, 1992,2:105–210
Bishop C M. Improving the generalization properties of radial basis function neural network. Neural Computation, 1991,3(4):579–588
Liu Jianqin. Artificial life theory and application (in Chinese) Beijing: Metallurgy Industry Press, 1997. 136–142
Chen S, Billings S A, Cowan C F N, et al. Practical identification of NARMAX models using radial basis functions. Int J Control, 1990,52(6):1327–1350
Lucasius C B, Kateman G. Towards solving subset selection problems with the aid of the genetic algorithm. In Manner R, Manderick B, eds. Parallel Problem Solving from Nature (Vol. 2). Amsterdam: Elsevier Science Publishers, 1992
Fonseca C M, Mendes E M, Fleming P J, et al. Non-linear model term selection with genetic algorithms. Proceedings of IEE/IEEE Workshop on Natural Algorithms in Signal Processing, 1993,2:271–278
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Synopsis of the first author Liu Meiqin, Doctoral student, born in 1972, majoring in control theory and control engineering.
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Liu, M., Chen, J. Determining the structures and parameters of radial basis function neural networks using improved genetic algorithms. J Cent. South Univ. Technol. 5, 141–146 (1998). https://doi.org/10.1007/s11771-998-0057-0
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DOI: https://doi.org/10.1007/s11771-998-0057-0