关键词: 粒子群, 神经网络, 运动特性, 目标搜索, 方向因子

Abstract: Aiming at the existing particle swarm artificial neural network has some problems of large errors and not easy to converge, a neural network target search algorithm is proposed under dynamic motion characteristics of particle swarm. It improves the traditional PSO algorithm by the direction factor. Combined with the motion state, it develops a maximum speed constraint mechanism to ensure the quality of the particle search. According to the particle spacing and fitness, it proposes the premature judgment mechanism to ensure the convergence effect. The experimental results show that this algorithm can keep better accuracy and convergence effect in different benchmark functions. It effectively demonstrates the feasibility of this algorithm.

Key words: particle swarm, neural network, motion characteristics, target acquisition, direction factor