摘要
针对逼近理想点排序法(technique for order preference by similarity to ideal solution,TOPSIS)存在的缺陷,提出基于Tanimoto系数和基于对称差的2种改进TOPSIS。改善或解决TOPSIS存在指标相关性问题、特殊样本集合无法比较优劣问题和样本数据动态变化时产生的逆序现象等缺陷;在稳定性、特异性、敏感性和有效性4方面对经典TOPSIS模型、改进Tanimoto模型和改进对称差模型进行对比验证,给出2种改进模型的适用场景。结果表明,2种方法各具有一定的优势。
Aiming at the defects of TOPSIS(technique for order preference by similarity to ideal solution),two improved TOPSIS methods based on Tanimoto coefficient and symmetric difference are proposed.Improve or solve TOPSIS index correlation problem,special sample set can not compare the advantages and disadvantages of the problem and the sample data dynamic changes in the reverse phenomenon and other defects;The classical TOPSIS model,the improved Tanimoto model and the improved symmetric difference model were compared and verified in terms of stability,specificity,sensitivity and validity,and the application scenarios of the two improved models were given.The results show that the two methods have their own advantages.
作者
常青
刘德生
杨阳
Chang Qing;Liu Desheng;Yang Yang(Science and Technology on Complex Electronic System Simulation Laboratory,Space Engineering University,Beijing 101416,China)
出处
《兵工自动化》
北大核心
2024年第6期49-55,共7页
Ordnance Industry Automation
基金
复杂电子系统仿真实验室基础研究项目(DXZT-JC-ZZ-2020-006)。
