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兰天宇, 钟艳如, 李清扬. 基于新型分值函数的多属性决策方法[J]. 桂林电子科技大学学报, xxxx, x(x): 1-8. DOI: 10.3969/1673-808X.202320
引用本文: 兰天宇, 钟艳如, 李清扬. 基于新型分值函数的多属性决策方法[J]. 桂林电子科技大学学报, xxxx, x(x): 1-8. DOI: 10.3969/1673-808X.202320
LAN Tianyu, ZHONG Yanru, LI Qingyang. Multiple attribute decision making based on novel score function[J]. Journal of Guilin University of Electronic Technology, xxxx, x(x): 1-8. DOI: 10.3969/1673-808X.202320
Citation: LAN Tianyu, ZHONG Yanru, LI Qingyang. Multiple attribute decision making based on novel score function[J]. Journal of Guilin University of Electronic Technology, xxxx, x(x): 1-8. DOI: 10.3969/1673-808X.202320

基于新型分值函数的多属性决策方法

Multiple attribute decision making based on novel score function

  • 摘要: 计算图片模糊值的分值和精度是多属性决策问题中的关键步骤,这一步骤的重要工具是分值函数和精度函数。现有函数面临着2个问题:一是对于一些明显不同的图片模糊值,现有函数的计算结果却与基础理论相悖;二是将分值函数和精度函数一起用于决策过程十分繁琐。为解决以上问题,提出了一种计算结果始终符合基础定义且不用借助精度函数的新型分值函数。同时,对新型分值函数的性质进行讨论和证明。此外,提出使用新型分值函数的多属性决策方法,与使用现有函数的多属性决策方法进行实例比较和分析,实验结果表明,使用新型分值函数的决策方法仍然可以取得正确的决策结果。最终证明所提出的分值函数比现有函数具有更高的计算准确性,运用于多属性决策问题中时可以保持其有效性。

     

    Abstract: Calculating the score and accuracy of the picture fuzzy value is a key step in the multiple attribute decision making problem. The important tools of this step are the score function and accuracy function. The existing functions are faced with two problems: on the one hand, for some obviously different picture fuzzy value, the calculation results of the existing functions are contrary to the basic theory; on the other hand, it is very cumbersome to use the score function and precision function together in the multiple attribute decision making. In order to solve the above problems, a novel score function whose calculation results always conform to the basic definition without the aid of precision function is proposed. At the same time, the properties of the novel score function are discussed and proved. In addition, the multiple attribute decision making method using the novel score function is proposed and compared with the existing multiple attribute decision making method using the existing function. The experimental results show that the method using the new score function can still obtain the correct decision results. Finally, it is proved that the proposed score function has higher computational accuracy than the existing function, and can maintain its effectiveness when applied to multiple attribute decision making problems.

     

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