基于EPSO-BP 的Elman 网络及其在飞行轨迹预测中的应用

针对BP 训练方式采用梯度法易导致局部收敛的不足, 提出一种融合进食粒子群算法(EPSO) 和梯度法的Elman 网络优化方法. 首先, 通过模拟鸟群进食行为得到一种EPSO 算法, 以改善标准PSO 的全局性能; 然后, 将EPSO 用于Elman 网络权值的全局优化, 同时将梯度法用于EPSO 的进食过程局部搜索, 以提高解的局部收敛性能; 最后, 将该网络优化方法用于飞行轨迹预测实验, 仿真结果表明了其有效性.

Elman network based on EPSO-BP and its application in flight trajectory prediction

To overcome the local convergence shortcoming of the gradient method used by the BP approach in the Elmannetwork training process, a combined training method of the exhaustion particle swarm optimization(EPSO) and gradient method is built. An enhanced PSO algorithm(EPSO), which has better global property, is proposed by simulating feeding behavior of birds. Then, the EPSO algorithm is applied to optimizing Elman network weights across the whole solution space, and the gradient method is used in the exhaustion process of EPSO to improve the local convergence property. Finally, the proposed training method is applied to flight trajectory prediction experiment, and simulation results show the effectiveness of this method.