Wavelet neural networks for adaptive equalization

A structure based on the wavelet neural networks is proposed for nonlinear channel equalization in a digital communication system, the minimum error probability (MEP) is applied as performance criterion to update the weighting matrix of wavelet networks. Our experimental results show that performance of the proposed wavelet networks based on equalizer can significantly improve the neural modeling accuracy and outperform the conventional neural networks in signal to noise ratio and channel non-linearity.