Aiming at the contradiction between the convergence rate and mean square error of traditional Constant Modulus Algorithm (CMA)

a hybrid wavelet neural network blind equalization algorithm based on fuzzy neural network controlling (FHWNN) is proposed.In this proposed algorithm

a transversal filter is cascaded to the front end of the wavelet network input layer

outputs of the transversal filter nodes are divided into real and imaginary parts

these two parts signals are merged into one complex signal after passing wavelet network.The proposed algorithm can improve the control precision of step-size via using fuzzy rules of Fuzzy Neural Network (FNN) to control the step-size of scale factor and displacement factor of the WNN.The weight coefficients iterative formulas of the transversal filter and the wavelet neural network are obtained via constant modulus cost function.The theory analysis and simulation result demonstrate that the proposed algorithm has faster convergence rate and smaller steady-state error.Accordingly

it can overcome the contradiction between the convergence rate and mean square error effectively.