Blind equalization based on neural network by linear correction

Blind equalization based on the neural network with a linear correction is proposed in this paper.A lateral filter adds before the input layer of neural network,then,the output signal from the node of the lateral filter is taken as the input signal of neural network.The instantaneous output error of the lateral filter and neural network blind equalization,which can be obtained by the constant modulus cost function,is used for adjusting error to update the weight coefficients of the lateral filter and neural network.This algorithm carries out the combination of linear and nonlinear optimization on non-convexity error surface.Simulation results show that the method of blind equalization in this paper provides higher convergence rate and better performance.