基于PSO-BP的应急通信感知装备效能评价方法

应急通信感知装备效能评价可支撑相关装备的发展规划, 而现有评价方法主观性强, 且自适应能力有待提升。因此, 提出一种基于粒子群优化(particle swarm optimization, PSO)算法的改进反向传播(back propagation, BP)神经网络的应急通信感知装备效能评价方法, 旨在建立客观精准的效能评价。首先面向实战效能构建了三级效能评价指标体系, 然后将样本数据进行主成分分析法降维, 建立BP神经网络回归模型, 并结合PSO算法对模型的连接权值与阈值进行优化, 形成PSO-BP模型以避免局部极小值问题, 获得可评价具体装备效能时的神经网络模型。实例分析表明, PSO-BP相较于BP神经网络模型评价的均方误差减少了28.18%, 表明PSO-BP模型具有更高的准确性。

Effectiveness evaluation method of emergency communication and sensing equipment based on PSO-BP

The effectiveness evaluation of emergency communication and sensing equipment can support the development plan of corresponding equipment, while the existing evaluation methods are highly subjective and the adaptive capabilities need to be improved. Therefore, an improved back propagation (BP) neural network based on particle swarm optimization (PSO) algorithm for the evaluation of the effectiveness of emergency communication and sensing equipment is proposed. The method aims to establish objective and accurate effectiveness evaluation. Firstly, a three-level effectiveness evaluation index system is established which oriented to actual combat effectiveness. Secondly, the sample data is reduced in dimensionality through principal component analysis (PCA). Next, a BP neural network regression model is built, the weights and thresholds of BP neural network are optimized by the particle swarm algorithm and a PSO-BP neural network model is formed to avoid the local minimum problem. Finally, a neural network model that can evaluate the effectiveness of specific equipment is obtained. The case analysis shows that compared with the BP neural network model, the mean square error of PSO-BP is reduced by 28.18%. The results show that the PSO-BP model has higher accuracy.