Study on multi-target fuzzy partitional cluster algorithm in air combat

doi:10.3778/j.issn.1002-8331.2009.28.071

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (28): 236-239.DOI: 10.3778/j.issn.1002-8331.2009.28.071

• 工程与应用 • Previous Articles Next Articles

Study on multi-target fuzzy partitional cluster algorithm in air combat

ZHANG Kun，ZHOU De-yun

Department of Electronic Information，Northwestern Polytechnical University，Xi’an 710072，China

Received:2008-05-27 Revised:2008-09-18 Online:2009-10-01 Published:2009-10-01
Contact: ZHANG Kun

空战多目标模糊聚类算法研究

张堃，周德云

西北工业大学电子信息学院，西安 710072

通讯作者: 张堃

Abstract

Abstract: The method of fuzzy partitional cluster algorithm is applied to classify the multi-target in the air combat，to be aimed at the shortcoming of traditional algorithm in calculating attack strategy difficultly.This algorithm reduces the computational complexity，and raises the operating speed by considering multi-target as a whole.Finally，multi-target colonies attack mathematical model is established，and uses the FCM（Fuzzy C-means） algorithm and improves FCM algorithm with different metrics based on two different multi-targets data，and divides them into several colonies according to the simulation in different targets.The simulation result indicates the algorithm’s validity and applicability in different situation.

Key words: partition, fuzzy cluster, Fuzzy C-Means（FCM）, metric, multi-target

摘要： 针对传统多机多目标攻击不易解算攻击任务分配，且计算量大的问题，提出基于划分的多目标模糊聚类算法，该算法根据目标属性的相似性进行多目标分类，可以有效地降低多目标任务分配解算维数，减少运算量，提高解算速度。采用FCM算法以及改进FCM算法度量方式构成的其他各个不同算法，建立空战多目标模糊聚类数学模型，对两组不同数据进行仿真分析，得到不同情况下的各算法的优劣性及适用性。

关键词: 划分, 模糊聚类, 模糊C均值, 度量方式, 多目标

CLC Number:

V271

TP18

ZHANG Kun，ZHOU De-yun. Study on multi-target fuzzy partitional cluster algorithm in air combat[J]. Computer Engineering and Applications, 2009, 45(28): 236-239.

张堃，周德云. 空战多目标模糊聚类算法研究[J]. 计算机工程与应用, 2009, 45(28): 236-239.

[1]	LAN Hong, HUANG Min. Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 81-88.
[2]	ZHU Juntao, YAO Guangle, ZHANG Gexiang, LI Jun, YANG Qiang, WANG Sheng, YE Shaoze. Survey of Few Shot Learning of Deep Neural Network [J]. Computer Engineering and Applications, 2021, 57(7): 22-33.
[3]	LI Hui, ZHANG Tianyuan, JIN Shuyu. Social Emotion Mining in Ancient Chinese Metrical Poetry [J]. Computer Engineering and Applications, 2021, 57(7): 171-177.
[4]	YANG Fengyu, HUANG Yaxuan, ZHOU Shijian, ZHENG Wei. Survey of Software Defect Prediction Combined with Multi-metrics [J]. Computer Engineering and Applications, 2021, 57(5): 10-24.
[5]	PENG Zhaoyong, WU Quan, CHEN Huawei, ZHENG Yue, WANG Shuxiang. Review of Research on Machine Vision Defect Detection Based on Literature Measurement [J]. Computer Engineering and Applications, 2021, 57(4): 28-34.
[6]	ZHANG Cuijun, AN Ran, MA Li. Building Change Detection in Remote Sensing Image Based on Improved U-Net [J]. Computer Engineering and Applications, 2021, 57(3): 239-246.
[7]	LI Ya, HOU Yandong, LIU Chang. Adaptive Optimal Fault-Tolerant Control Based on Fault Degree [J]. Computer Engineering and Applications, 2021, 57(23): 295-302.
[8]	CHANG Yawen, ZHAO Dongqing, SHAN Yanhu. Research on Stereo Matching Algorithm Based on Multi-feature Fusion and Adaptive Aggregation [J]. Computer Engineering and Applications, 2021, 57(23): 219-225.
[9]	ZHANG Kehua, CHENG Weiqing. Differential Privacy Clustering Algorithm Based on Spatial Dynamic Partition [J]. Computer Engineering and Applications, 2021, 57(2): 97-103.
[10]	MA Chengqi, LI Xuehua, ZHANG Lanjie, XIANG Wei. Anti-oclusion Monocular Depth Estimation Algorithm [J]. Computer Engineering and Applications, 2021, 57(2): 217-222.
[11]	LI Xiaohua, SU Jun, LI Xiuxiu. Underwater Bearing-Only Multi-target Tracking in Dense Clutter Environment Using Single Observation [J]. Computer Engineering and Applications, 2021, 57(17): 253-259.
[12]	HE Jiao, TAN Dailun. Three-Dimensional Terrain Path Planning Based on Sight Range and Genetic Algorithm [J]. Computer Engineering and Applications, 2021, 57(15): 279-285.
[13]	HUANG Tongyu, HU Binjie, ZHU Tingting, HUANG Zhewen. Object Detection Method Based on Multi-scale Feature Fusion for Driving Scene [J]. Computer Engineering and Applications, 2021, 57(14): 134-141.
[14]	ZHANG Xiaoxiao, NIU Fu, MAO Jianping, AN Jubai, GUO Hao. Research on Identification of Sea Surface Oil Spill Zone Based on Multi-feature Fusion and Residual Network [J]. Computer Engineering and Applications, 2021, 57(14): 267-274.
[15]	CHEN Xinqiang, LING Jun, QI Lei, YANG Yongsheng, ZHOU Yamin. Ship Tracking Mechanism via Multi-dimension Features Fusion and Scale Estimation [J]. Computer Engineering and Applications, 2021, 57(13): 246-250.

Study on multi-target fuzzy partitional cluster algorithm in air combat

空战多目标模糊聚类算法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics