区间值直觉模糊麦克劳林聚合算子的近红外反射波段划分方法

doi:10.3964/j.issn.1000-0593(2024)06-1731-09

Abstract
References
Related Citation (15)

Download: PDF (1588 KB)
Export: BibTeX | EndNote (RIS)

Abstract Using different wavelength spectra to identify different types of foreign fibers can effectively eliminate foreign fibers and increase the detection rate. Given the problem of a single evaluation index existing in traditional band division methods, the interaction of attribute indexes of different fibers is studied, combined with the advantages of the multi-attribute group decision-making (MAGDM) method, this paper proposes to use the MAGDM method to realize the selection of the optimal detection band for foreign fibers in cotton. According to the relation of attribute indexes of different fibers, the inter-class separability, correlation and ABS index are determined as attribute evaluation indexes. Firstly, to solve the problem of inaccurate evaluation criteria in the MAGDM method, a system of evaluation criteria function linear equations is constructed so that the rank of the augmented matrix is equal to the number of unknowns, ensuring that the equation system has a unique solution, thereby improving the accuracy of the decision result. Next, the power mean (PA) operator is used to eliminate the adverse effects of unreasonable evaluation information values on decision results, combined with the Maclaurin symmetric mean (MSM) operator to comprehensively consider the relationship between input arguments, deriving the weighted interval-valued intuitionistic fuzzy power Maclaurin symmetric mean (WIVIFPMSM) aggregation operator.Then, the TOPSIS method is used to determine the weight information of foreign fibers, the evaluation information of various attributes of different foreign fibers is aggregated, and the decision results are chosenaccording to the established evaluation criteria. Thus, a MAGDM method based on interval-valued intuitionistic fuzzy sets (IVIFSs) is constructed to realize the optimal band selection of various attributes of foreign fibers. Moreover, the WIVIFPMSM aggregation operator is compared with the inter-class separability band selection (ISBC) method and adaptive band selection (ABS) method, the influence of different band division methods on the results are analyzed, and the existing problems and deficiencies in existing research are summarised. To improve the decision accuracy of the MAGDM method, parameterks influence on decision results is analysed, and it is proved that the IVIFPMSM aggregation method has better stability, which provides a new idea for the study of band division of foreign fibers in complex environments. Finally, it is verified through experiments that the near-infrared bandW₃: 780～1 100 nm is the optimal detection band. In addition, this paper has a specificguiding significance for the theoretical extension of band selection and the application of MAGDM methods.

Key words：Foreign fiber;Reflectance spectrum;WIVIFPMSM operator;Multi-attribute decision making;Band selection

Received: 2021-11-25 Accepted: 2023-11-02

ZTFLH:

TP391.41

Corresponding Authors: DU Yu-qin E-mail: duyugin@ucass.edu.cn

Cite this article:

REN Wei-jia,DU Xiang-jun,DU Yu-qin, et al. Near Infrared Reflection Band Division Method Based on Interval-Valued Intuitionistic Fuzzy Maclaurin Symmetric Mean Aggregation Operator[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1731-1739.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)06-1731-09 OR https://www.gpxygpfx.com/EN/Y2024/V44/I06/1731

[1] REN Wei-jia, DU Yu-hong, ZUO Heng-li, et al(任维佳, 杜玉红, 左恒力, 等). Journal of Textile Research(纺织学报), 2021, 42(12): 196.
[2] ZHANG Lin, WEI Ping, WU Jian-bo, et al(张林, 韦平, 伍剑波, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(15): 289.
[3] SHI Hong-yu, GUAN Sheng-qi(师红宇, 管声启). Journal of Donghua University(Natural Science)[东华大学学报(自然科学版)], 2016, 42(3): 400.
[4] Bao S, Cao C, Chen W, et al. Geocarto International, 2021, 36(2): 226.
[5] Charifi R, Es-Sbai N, Zennayi Y, et al. Computers & Geosciences, 2021, 150: 104696.
[6] YANG Wen-zhu, LI Dao-liang, WEI Xin-hua, et al(杨文柱, 李道亮, 魏新华, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(10): 186.
[7] JIA Dong-yao, DING Tian-huai(郏东耀, 丁天怀). Journal of Tsinghua University(Science and Technology)[清华大学学报(自然科学版)]，2005, 2: 193.
[8] Jiang Y, Li C. Computers and Electronics in Agriculture, 2015, 119: 191.
[9] Wang Q, Li Q, Li X. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2019, 12(12): 4940.
[10] YANG Sai, ZHU Qi-bing, HUANG Min(杨赛, 朱启兵, 黄敏). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(3): 990.
[11] MA Meng-ru, ZHANG Yong-bin, WANG Yi-dan(马梦茹, 张永彬, 王奕丹). Journal of North China University of Science and Technology(Natural Science Edition)[华北理工大学学报(自然科学版)], 2021, 43(3): 20.
[12] Ali Z, Mahmood T. Computational and Applied Mathematics, 2020, 39: 161.
[13] Yager R R. IEEE Transactions on Systems, 2001, 31(6): 724.
[14] Zindani D, Maity S R, Bhowmik S. Soft Computing, 2020, 24(18): 114091.
[15] Liu Z, Teng F, Liu P, et al. International Journal for Uncertainty Quantifications, 2018, 8(3): 211.
[16] Garg H, Ali Z, Mahmood T. Expert Systems, 2021, 38(1): e12609.
[17] Ren W, Du Y. Journal of Natural Fibers, 2023, 20(2): 2219906.