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A high order feed forward neural network architecture with optimum number of nodes is used for adaptive channel equalization in this paper. The replacement of summation at each node by multiplication results in more powerful mapping because of its capability of processing higher-order information from training data. The equalizer is tested on Rayleigh fading channel with BPSK signals. Performance comparisons with Recurrent Radial Basis Function (RRBF) neural network show that the proposed equalizer provides compact architecture and satisfactory results in terms of bit error rate performance at various levels of signal to noise ratios for a Rayleigh fading channel.
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