Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (11): 3632-3640.DOI: 10.11772/j.issn.1001-9081.2022101605
Special Issue: 前沿与综合应用
• Frontier and comprehensive applications • Previous Articles Next Articles
Xiangxi WEN1,2, Yating PENG1,2, Kexin BI3, Yuming HENG1,2, Minggong WU1,2()
Received:
2022-10-26
Revised:
2023-01-03
Accepted:
2023-01-09
Online:
2023-04-12
Published:
2023-11-10
Contact:
Minggong WU
About author:
WEN Xiangxi, born in 1984, Ph. D., associate professor. His research interests include air traffic control automation.Supported by:
温祥西1,2, 彭娅婷1,2, 毕可心3, 衡宇铭1,2, 吴明功1,2()
通讯作者:
吴明功
作者简介:
温祥西(1984—),男,江苏连云港人,副教授,博士,主要研究方向:空管自动化基金资助:
CLC Number:
Xiangxi WEN, Yating PENG, Kexin BI, Yuming HENG, Minggong WU. Situation prediction of flight conflict network based on online fuzzy least squares support vector machine with optimal training set[J]. Journal of Computer Applications, 2023, 43(11): 3632-3640.
温祥西, 彭娅婷, 毕可心, 衡宇铭, 吴明功. 基于最优样本集在线模糊最小二乘支持向量机的飞行冲突网络态势预测[J]. 《计算机应用》唯一官方网站, 2023, 43(11): 3632-3640.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022101605
演化次数 | 集聚系数 | 平均路径长度 | 鲁棒性 | 网络效率 |
---|---|---|---|---|
1 | 0.456 | 0.085 | 0.998 | 0.136 |
2 | 0.414 | 0.091 | 1.058 | 0.142 |
3 | 0.478 | 0.086 | 0.987 | 0.145 |
4 | 0.438 | 0.080 | 0.920 | 0.142 |
5 | 0.331 | 0.082 | 0.929 | 0.133 |
6 | 0.326 | 0.075 | 0.923 | 0.113 |
0.346 | 0.065 | 0.902 | 0.089 | |
︙ | ︙ | ︙ | ︙ | ︙ |
1 998 | 0.374 | 0.054 | 0.789 | 0.084 |
1 999 | 0.366 | 0.052 | 0.780 | 0.084 |
2 000 | 0.380 | 0.053 | 0.813 | 0.081 |
Tab. 1 Time series of network indicators
演化次数 | 集聚系数 | 平均路径长度 | 鲁棒性 | 网络效率 |
---|---|---|---|---|
1 | 0.456 | 0.085 | 0.998 | 0.136 |
2 | 0.414 | 0.091 | 1.058 | 0.142 |
3 | 0.478 | 0.086 | 0.987 | 0.145 |
4 | 0.438 | 0.080 | 0.920 | 0.142 |
5 | 0.331 | 0.082 | 0.929 | 0.133 |
6 | 0.326 | 0.075 | 0.923 | 0.113 |
0.346 | 0.065 | 0.902 | 0.089 | |
︙ | ︙ | ︙ | ︙ | ︙ |
1 998 | 0.374 | 0.054 | 0.789 | 0.084 |
1 999 | 0.366 | 0.052 | 0.780 | 0.084 |
2 000 | 0.380 | 0.053 | 0.813 | 0.081 |
样本序列 | 嵌入维 | 时间延迟/s | 最大Lyapunov指数 |
---|---|---|---|
集聚系数 | 3 | 11 | 0.499 |
平均路径长度 | 4 | 13 | 0.309 |
鲁棒性 | 3 | 17 | 0.211 |
网络效率 | 4 | 9 | 0.342 |
Tab. 2 Maximum Lyapunov exponents for each time series
样本序列 | 嵌入维 | 时间延迟/s | 最大Lyapunov指数 |
---|---|---|---|
集聚系数 | 3 | 11 | 0.499 |
平均路径长度 | 4 | 13 | 0.309 |
鲁棒性 | 3 | 17 | 0.211 |
网络效率 | 4 | 9 | 0.342 |
数据集序号 | 空域大小/km3 | 航空 器数 | 演化规则 | 数据集 样本数 | ||
---|---|---|---|---|---|---|
进入 概率/% | 间隔 时间/s | 改变高度 概率/% | ||||
1 | 100×100×0.6 | 50 | 60 | 8 | 30 | 3 000 |
2 | 120×120×0.3 | 55 | 50 | 10 | 40 | 2 500 |
3 | 150×150×0.6 | 80 | 40 | 10 | 20 | 3 000 |
Tab. 3 Dataset generation rules
数据集序号 | 空域大小/km3 | 航空 器数 | 演化规则 | 数据集 样本数 | ||
---|---|---|---|---|---|---|
进入 概率/% | 间隔 时间/s | 改变高度 概率/% | ||||
1 | 100×100×0.6 | 50 | 60 | 8 | 30 | 3 000 |
2 | 120×120×0.3 | 55 | 50 | 10 | 40 | 2 500 |
3 | 150×150×0.6 | 80 | 40 | 10 | 20 | 3 000 |
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