[1]周久海,严洪森,张振涛. 基于FSVR的海洋环境下武器效能评估[J].计算机技术与发展,2015,25(01):24-28.
　ZHOU Jiu-hai,YAN Hong-sen,ZHANG Zhen-tao. Operational Effectiveness Evaluation of Weapon under Marine Environment Based on FSVR[J].,2015,25(01):24-28.
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 基于FSVR的海洋环境下武器效能评估()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年01期
页码:
24-28
栏目:
智能、算法、系统工程
出版日期:
2015-01-10

文章信息/Info

Title:
 Operational Effectiveness Evaluation of Weapon under Marine Environment Based on FSVR
文章编号:
1673-629X（2015)01-0024-05
作者:
 周久海; 严洪森; 张振涛
 东南大学 复杂工程系统测量与控制教育部重点实验室； 东南大学 自动化学院
Author(s):
 ZHOU Jiu-hai ; YAN Hong-sen ; ZHANG Zhen-tao
关键词:
 ; 作战效能; 模糊支持向量机; 回归; 评估模型; 海洋环境
Keywords:
 ; operational effectiveness; FSVR; regression; evaluation model; marine environment
分类号:
TP391
文献标志码:
A
摘要:
 海洋环境中各种气象水文要素对海军武器装备的作战效能影响显著,且影响机理复杂,这大大增加了海军武器装备作战效能的评估难度。针对武器装备作战效能评估问题中的小样本、非线性和高维度等问题,将支持向量回归机( Sup-port Vector Regression,SVR)模型应用到该作战效能评估问题中会有较好的性能表现。标准的支持向量回归机对每个样本公平对待,而实际中的样本存在重要性的差别,文中将模糊支持向量回归机应用到武器作战效能评估问题中,提出一种根据样本与所有测试样本在高维特征空间的欧氏距离之和的大小来确定每个样本模糊隶属度的方法,从而体现不同的样本对决策函数学习的贡献率差异。实验结果表明,对于样本中存在野点和噪声的回归问题,模糊支持向量机表现出了更高的评估精度。
Abstract:
 Operational effectiveness of naval weapon equipment is influenced by the meteorological and hydrological elements of marine environment with complex mechanism,which greatly enhances the difficulty of its operational effectiveness evaluation. Aiming at the non-linear and multi-dimension problem with small samples in the weapon equipment operational effectiveness evaluation,Support Vector Re-gression ( SVR) model is applied to the operational effectiveness evaluation of weapon to make good performances. In the theory of SVR,all the training samples are treated uniformly,but in real-world applications,the effects of the training samples are different. In this paper,introduce Fuzzy Support Vector Regression ( FSVR) to problems of operational effectiveness evaluation of weapon and propose a method of giving each sample a fuzzy membership according to the sum of the distances from the sample and all the test samples in the high dimensional feature space,so that different samples can make different contributions to the learning of decision function. Experimen-tal results show that FSVR shows a higher evaluating accuracy in regression problems with outliers or noises in the samples.

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更新日期/Last Update: 2015-04-17