A neural network equalizer with the fuzzy decision learning rule

We propose a neural network equalizer with a fuzzy decision learning rule based on the generalized probabilistic descent algorithm with the minimum decision error formulation. The neural network used is the multi-layer perceptron. It is shown that the decision region overlapped by noise can be overcome by the use of a fuzzy decision learning rule based on the generalized probabilistic descent algorithm. We apply this algorithm to a neural network equalizer with binary sequences in a nonlinear distortion channel. Simulation results confirm that the fuzzy decision learning algorithm works more effectively than hard decision learning algorithms when the learning patterns are not separable by high additive noise.