Abstract
A Growing and Pruning Radial Basis Function (GAP-RBF) network has been recently proposed by Huang et al. [IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 34(6) (2004), 2284–2292]. However, its performance in signal processing areas is not clear yet. In this paper, GAP-RBF network is used for solving the communication channel equalization problem. The simulation results demonstrate that GAP-RBF equalizer outperforms other equalizers such as recurrent neural network and MRAN on linear and nonlinear channel model in terms of bit error rate.
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Li, MB., Huang, GB., Saratchandran, P. et al. Performance Evaluation of GAP-RBF Network in Channel Equalization. Neural Process Lett 22, 223–233 (2005). https://doi.org/10.1007/s11063-005-6799-x
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DOI: https://doi.org/10.1007/s11063-005-6799-x