• • 上一篇    

一致性和特征向量驱动的个性化语义及其在稻米评价中的应用

刘瑛1, 魏海燕2, 魏翠萍1   

  1. 1. 扬州大学数学科学学院, 扬州 225002;
    2. 扬州大学农学院, 扬州 225002
  • 收稿日期:2022-03-26 修回日期:2022-05-13 发布日期:2022-11-04
  • 基金资助:
    国家自然科学基金项目(71971190)资助课题.

刘瑛, 魏海燕, 魏翠萍. 一致性和特征向量驱动的个性化语义及其在稻米评价中的应用[J]. 系统科学与数学, 2022, 42(10): 2680-2697.

LIU Ying, WEI Haiyan, WEI Cuiping. Consistency-Eigenvector-Driven Personalized Individual Semantics and Its Application in Rice Evaluation[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(10): 2680-2697.

Consistency-Eigenvector-Driven Personalized Individual Semantics and Its Application in Rice Evaluation

LIU Ying1, WEI Haiyan2, WEI Cuiping1   

  1. 1. College of Mathematical Sciences, Yangzhou University, Yangzhou 225002;
    2. College of Agriculture, Yangzhou University, Yangzhou 225002
  • Received:2022-03-26 Revised:2022-05-13 Published:2022-11-04
在语言决策过程中,不同决策者对同一语言术语的理解不同,因此,考虑决策者的个性化语义对决策结果的合理性具有重要意义.文章在语言分布评估决策矩阵和模糊偏好关系环境下,构建个性化语义的导出模型.从决策矩阵导出的排序向量构建满足积型一致性的成对比较矩阵.然后基于模糊判断矩阵的积型一致性和特征向量的性质,从整合决策矩阵客观信息和偏好关系主观信息的思想出发,提出导出决策者个性化语义函数的两种方法:积型一致性方法和特征向量法.最后将个体评价信息集结得到方案的最终排序.文章最后结合感官评价法,将所提方法运用到对稻米进行评价的实际决策问题中,并和已有的个性化语义导出方法进行比较,说明其合理性与有效性.
In the process of linguistic decision-making,different decision-makers have different understandings of the same linguistic term,so it is of great significance to consider the personalized individual semantics of decision-makers for the rationality of decision-making results.In this paper,under the environment of linguistic distribution assessments decision matrix and fuzzy preference relation,we construct model to derive personalized individual semantics.A pairwise comparison matrix that satisfies multiplicative consistency is constructed from the comprehensive ranking vector derived from the decision matrix.Then,based on the multiplicative consistency of the fuzzy judgment matrix and the properties of the eigenvectors,starting from the idea of integrating the objective information of the decision matrix and the subjective information of the preference relationship,two methods are proposed to derive the decision maker's personalized individual semantic function:Multiplicative consistency method and eigenvector method.Finally,the individual evaluation information is aggregated to obtain the final ranking of the alternatives.combined the sensory evaluation method,the proposed method are applied to the actual decision-making problem of rice evaluation,and compared with the existing personalized individual semantic derivation methods to show its rationality and validity.

MR(2010)主题分类: 

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[1] Zadeh L A. The concept of a linguistic variable and its applications to approximate reasoning-1. Information Sciences, 1975, 8(3):199-249.
[2] Yager R. Aggregation of ordinal information. Fuzzy Optimization and Decision Making, 2007, 6(3):199-219.
[3] Herrera F, Martínez L. A-2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 2000, 8(6):746-752.
[4] Wang J H, Hao J. A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 2006, 14(3):435-445.
[5] Dong Y C, Xu Y F. Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model. IEEE Transactions on Fuzzy Systems, 2009, 17(6):1366-1378.
[6] Rodríguez R M, Martínez L, Herrera F. Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 2011, 20(1):109-119.
[7] Zhang G Q, Dong Y C, Xu Y F. Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion, 2014, 17:46-55.
[8] Wei C P, Liao H C. A multigranularity linguistic group decision-making method based on hesitant 2-tuple sets. International Journal of Intelligent Systems, 2016, 31(6):612-634.
[9] Li C C, Dong Y C, Herrera F. Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Information Fusion, 2017, 33:29-40.
[10] Li C C, Rodriguez R M, Martinez L, et al. Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic. Knowledge-Based Systems, 2018, 145:156-165.
[11] Li C C, Dong Y, Herrera F. A consensus model for large-scale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups. IEEE Transactions on Fuzzy Systems, 2019, 27(2):221-233.
[12] Tang X A, Peng Z L, Zhang Q, et al. Consistency and consensus-driven model to personalize individual semantics of linguistic terms for supporting group decision making with distribution linguistic preference relations. Knowledge-Based Systems, 2020, 189:105078.
[13] Zhang H J, Li C C, Liu Y T, et al. Modeling personalized individual semantics and consensus in comparative linguistic expression preference relations with self-confidence:An optimizationbased approach. IEEE Transactions on Systems, 2021, 209(3):627-640.
[14] Zhang H J, Dong Y C. Personalized individual semantics-based approach for linguistic failure modes and effects analysis with incomplete preference information. IISE Transactions, 2020, 52(111):1275-1296.
[15] Tang X A, Yang S L, Pedrycz W. Derivation of personalized numerical scales from distribution linguistic preference relations:An expected consistency-based goal programming approach. Neural Computing and Applications, 2019, 31, https://doi.org/10.1007/s00521-019-04466-5.
[16] Fan Z P, Ma J, Zhang Q. An approach to multiple attribute decision making based on fuzzy preference information on alternatives. Fuzzy Sets and Systems, 2002, 131:101-106.
[17] Wang Y M, Parkan C. Multiple attribute decision making based on fuzzy preference information on alternatives:Ranking and weighting. Fuzzy Sets and Systems, 2005, 153:331-346.
[18] Tanino T. Fuzzy preference ordering in group decision making. Fuzzy Sets and Systems, 1984, 12:117-131.
[19] Herrera-Viedma E, Herrera F. Some issues on consistency of fuzzy preference relations. European Journal of Operational Research, 2004, 154(1):98-109.
[20] 中国国家标准化管理委员会,中华人民共和国国家质量监督检验检疫总局.粮油检验稻谷、大米蒸煮食味品质感官评价方法GB/T15682-2008.食品伙伴网, 2008-11-04.(China National Standardization Administration, General Administration of quality supervision, inspection and Quarantine of the people's Republic of China. Inspection of grain and oil sensory evaluation method for cooking and eating quality of rice and rice GB/T15682-2008. Food Partner Network, 2008-11-04.)
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