Nonlinear Haze Channel Equalization Algorithm Based on Improved Neural Network

Aiming at the problem of traditional neural network in nonlinear haze channel equalization application, this paper proposes a nonlinear haze channel equalization algorithm based on improved neural network. The algorithm uses directed search optimization as a training device for neural networks, and then uses directed search optimization algorithm to train nonlinear haze channel equalization. A new haze channel equalization strategy is proposed for the equalization of nonlinear haze channels. Finally, simulation experiments are carried out. The simulation experiments show that compared with the traditional BP, GA and PSO, the proposed algorithm uses the directional search update strategy, and the gene mutation technology can realize the local optimal equalizer with better performance.