Group Formation Basd on SATC-ALO and SOM Neural Network

基于SATC-ALO和SOM神经网络的机群编队分组

¹ Air Traffic Control and Narigation College Force Engineering University, Xi’an 710038, China
² 959939 PLA Troops, Cangzhou 0617136, China

Received: 26 January 2018

Abstract

Firstly, the problem of group-air grouping is analyzed to introduce the aircraft attribute grouping model and aircraft fuel consumption grouping model. Then, SATC-ALO optimized by Chaos optimization algorithm and Tournament Selection strategy and SOM neural network are used to solve the formation grouping model. Finally, comparative experiments of similarity calculation method and formation grouping method were performed with 50 groups of data. The experimental results show that hybrid method is superior to Euclidean distance method. SATC-ALO algorithm has the highest grouping accuracyand meets the real-time requirements. However, the number of groups needs to be specified in advance. The accuracy of SOM neural network grouping is slightly lower than SATC-ALO algorithm, but the grouping time is lower than SATC-ALO algorithm, and there is no need to specify the number of groups. Both SOM neural network and SATC-ALO algorithm can perfectly solve the problem of group-air grouping and have practical application value.

摘要

首先，分析机群编队分组问题，同时考虑了飞机属性分组模型和飞机油耗分组模型。然后，使用混沌优化算法和锦标赛选择策略优化后的SATC-ALO算法和SOM神经网络求解编队分组模型。最后，使用50组数据进行相似度计算方法和编队分组方法对比实验。实验结果表明，混合计算法方法优于欧式距离法，SATC-ALO算法分组精度最高，并且满足实时性要求，但需要事先指定分组数目，而SOM神经网络的分组精度稍低于SATC-ALO算法，但分组时间优于SATC-ALO算法，并且不需要指定分组数目。2种方法均可以更好地解决编队分组问题，具有实际应用价值。