论文:2019,Vol:37,Issue(2):407-416
引用本文:
杨任农, 张振兴, 房育寰, 俞利新, 左家亮. 基于SATC-ALO和SOM神经网络的机群编队分组[J]. 西北工业大学学报
YANG Rennong, ZHANG Zhenxing, FANG Yuhuan, YU Lixin, ZUO Jialiang. Group Formation Basd on SATC-ALO and SOM Neural Network[J]. Northwestern polytechnical university

基于SATC-ALO和SOM神经网络的机群编队分组
杨任农1, 张振兴1, 房育寰2, 俞利新1, 左家亮1
1. 空军工程大学 空管领航学院, 陕西 西安 710038;
2. 中国人民解放军95939部队, 河北 沧州 061736
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。
关键词:    机群编队分组    混合计算方法    自适应Tent混沌搜索蚁狮优化算法(SATC-ALO)    SOM神经网络   
Group Formation Basd on SATC-ALO and SOM Neural Network
YANG Rennong1, ZHANG Zhenxing1, FANG Yuhuan2, YU Lixin1, ZUO Jialiang1
1. Air Traffic Control and Narigation College Force Engineering University, Xi'an 710038, China;
2. 959939 PLA Troops, Cangzhou 0617136, China
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.
Key words:    group-air grouping    hybrid calculating method    self-adaptive tent chaos search ant lion optimizer algorithm(SATC-ALO)    self organizing maps network(SOM)   
收稿日期: 2018-01-26     修回日期:
DOI: 10.1051/jnwpu/20193720407
基金项目: 航空科学基金(20155196022)、国家自然科学基金青年基金(71501184)与陕西省自然科学基金(2016JQ6050)资助
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作者简介: 杨任农(1969-),空军工程大学教授、博士生导师,主要从事人工智能及机器学习研究。
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