Research of Blind Equalization Algorithm by Genetic Algorithm Optimizing BP Neural Network

In this paper, the theories of genetic algorithm (GA) and back propagation (BP) algorithm are introduced. For the purpose of overcoming the disadvantages of standard BP algorithm, such as local optimum and low convergence speed, the paper adopts genetic algorithm optimizing BP neural network for training. By analyzing computer stimulation results and comparing with traditional blind equalization algorithm, it shows that, the equalization effect of GA-BP has been greatly improved. For instance, the convergence speed is quickened, BER is reduced greatly and state residual error is decreased.