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RBF equalizer design using directional evolutionary multi-objective optimization

Zong, N. and Hong, X. (2004) RBF equalizer design using directional evolutionary multi-objective optimization. In: Chu, H. W., Savoie, M., Toraichi, K. and Kwan, P. (eds.) International Conference on Computing, Communications and Control Technologies, Vol 3, Proceedings. Int Inst Informatics & Systemics, Orlando, pp. 109-114. ISBN 9806560175

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Whilst radial basis function (RBF) equalizers have been employed to combat the linear and nonlinear distortions in modern communication systems, most of them do not take into account the equalizer's generalization capability. In this paper, it is firstly proposed that the. model's generalization capability can be improved by treating the modelling problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets. Then, as a modelling application, a new RBF equalizer learning scheme is introduced based on the directional evolutionary MOO (EMOO). Directional EMOO improves the computational efficiency of conventional EMOO, which has been widely applied in solving MOO problems, by explicitly making use of the directional information. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good performance not only on explaining the training samples but on predicting the unseen samples.

Item Type:Book or Report Section
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14518
Uncontrolled Keywords:channel equalization, RBF equalizer, multi-objective optimization, evolutionary algorithms, gradient descent
Additional Information:Proceedings Paper International Conference on Computing, Communications and Control Technologies (CCCT 2004) AUG 14-17, 2004 Austin, TX
Publisher:Int Inst Informatics & Systemics

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