Beklenen Aralığa Dayanan Aralık Tip II Üssel Bulanık Sayının Aralık Tip II Parametrik Yamuk Bulanık Sayı Yakınsaması
Loading...
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Open Access Color
GOLD
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
BIP! Indicators
Impulse
Average
Influence
Average
Popularity
Average
Abstract
Tip I bulanık sayıları belirsizliği ele almak için bazı karar verme problemlerinde kullanılmaktadır. Tip I bulanık sayılarının üyelik dereceleri adi sayılardır. Ancak gerçek yaşam problemlerinde üyelik derecelerinin bulanık sayılar ile gösterilebileceği olaylar var olabilir. Bu gibi durumlarda Tip II bulanık sayıları kullanılabilir. Bulanık sayının daha basit bir formunun kullanılması bazı çalışmalarda karmaşık hesaplamalardan kaçınmak için bir avantaj olarak görülmektedir. Bu durum dikkate alınarak bu çalışmada aralık Tip II üssel bulanık sayının aralık Tip II parametrik yamuk bulanık sayı yakınsaması beklenen aralıkların eşitliklerinin kullanıldığı bir kısıtlı optimizasyon problemi ile bulunmuş ve formüller verilmiştir.
Description
Keywords
Bilgisayar Bilimleri- Yazılım Mühendisliği-Bilgisayar Bilimleri- Yapay Zeka, Bilgisayar Bilimleri, Yazılım Mühendisliği, Bilgisayar Bilimleri, Yapay Zeka
Fields of Science
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
[1] Ban A. I. Coroianu L. & Khastan A. (2016). Conditioned weighted L–R approximations of fuzzy numbers. Fuzzy Sets and Systems 283 56-82. https://doi.org/10.1016/j.fss.2015.03.012[2] Nasibov E. N. & Peker S. (2008). On the nearest parametric approximation of a fuzzy number. Fuzzy Sets and Systems 159(11) 1365-1375. https://doi.org/10.1016/j.fss.2007.08.005[3] Hao Z. Xu Z. Zhao H. & Zhang R. (2021). The context-based distance measure for intuitionistic fuzzy set with application in marine energy transportation route decision making. Applied Soft Computing 101 Article 107044. https://doi.org/10.1016/j.asoc.2020.107044[4] Krawczak M. & Szkatuła G. (2020). On matching of intuitionistic fuzzy sets. Information Sciences 517 254-274. https://doi.org/10.1016/j.ins.2019.11.050[5] Alcantud J. C. R. Khameneh A. Z. & Kilicman A. (2020). Aggregation of infinite chains of intuitionistic fuzzy sets and their application to choices with temporal intuitionistic fuzzy information. Information Sciences 514 106-117. https://doi.org/10.1016/j.ins.2019.12.008[6] Sodenkamp M. A. Tavana M. & Di Caprio D. (2018). An aggregation method for solving group multi-criteria decision-making problems with single-valued neutrosophic sets. Applied Soft Computing 71 715-727. https://doi.org/10.1016/j.asoc.2018.07.020[7] Grzegorzewski P. (2002). Nearest interval approximation of a fuzzy number. Fuzzy Sets and systems 130(3) 321-330. https://doi.org/10.1016/S0165-0114(02)00098-2[8] Hai S. Gong Z. & Chen Z. (2020). Weighted pseudometric approximation of 2-dimensional fuzzy numbers by fuzzy 2-cell prismoid numbers preserving the centroid. Fuzzy Sets and Systems 387 158-173. https://doi.org/10.1016/j.fss.2018.12.013[9] Liu X. & Lin H. (2007). Parameterized approximation of fuzzy number with minimum variance weighting functions. Mathematical and computer modelling 46(11-12) 1398-1409. https://doi.org/10.1016/j.mcm.2007.01.011[10] Coroianu L. & Stefanini L. (2016). General approximation of fuzzy numbers by F-transform. Fuzzy Sets and Systems 288 46-74. https://doi.org/10.1016/j.fss.2015.03.015[11] Chanas S. (2001). On the interval approximation of a fuzzy number. Fuzzy Sets and Systems 122(2) 353-356. https://doi.org/10.1016/S0165-0114(00)00080-4[12] Coroianu L. Gagolewski M. & Grzegorzewski P. (2013). Nearest piecewise linear approximation of fuzzy numbers. Fuzzy Sets and Systems 233 26-51. https://doi.org/10.1016/j.fss.2013.02.005[13] Yeh C. T. & Chu H. M. (2014). Approximations by LR-type fuzzy numbers. Fuzzy Sets and Systems 257 23-40. https://doi.org/10.1016/j.fss.2013.09.004[14] Huang H. Wu C. Xie J. & Zhang D. (2017). Approximation of fuzzy numbers using the convolution method. Fuzzy Sets and Systems 310 14-46. https://doi.org/10.1016/j.fss.2016.06.010[15] Ban A. (2008). Approximation of fuzzy numbers by trapezoidal fuzzy numbers preserving the expected interval. Fuzzy Sets and Systems 159(11) 1327-1344. https://doi.org/10.1016/j.fss.2007.09.008[16] Ban A. I. & Coroianu L. (2015). Existence uniqueness calculus and properties of triangular approximations of fuzzy numbers under a general condition. International Journal of Approximate Reasoning 62 1-26. https://doi.org/10.1016/j.ijar.2015.05.004[17] Grzegorzewski P. & Pasternak-Winiarska K. (2014). Natural trapezoidal approximations of fuzzy numbers. Fuzzy Sets and Systems 250 90-109. https://doi.org/10.1016/j.fss.2014.03.003[18] Wang G. & Li J. (2017). Approximations of fuzzy numbers by step type fuzzy numbers. Fuzzy sets and systems 310 47-59. https://doi.org/10.1016/j.fss.2016.08.003[19] Zhou J. Miao D. Gao C. Lai Z. & Yue X. (2019). Constrained three-way approximations of fuzzy sets: From the perspective of minimal distance. Information Sciences 502 247-267. https://doi.org/10.1016/j.ins.2019.06.004[20] Ban A. I. & Coroianu L. (2012). Nearest interval triangular and trapezoidal approximation of a fuzzy number preserving ambiguity. International Journal of Approximate Reasoning 53(5) 805-836.[21] Peker S. & Nasibov E. (2019). Tip II genelleştirilmiş çan şekilli bulanık sayısının Tip II parametrik yamuk bulanık sayı yakınsanması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23(1) 163-169. https://doi.org/10.19113/sdufenbed.466901[22] Huang S. Zhao G. & Chen M. (2018). A fast analytical approximation type-reduction method for a class of spiked concave type-2 fuzzy sets. International Journal of Approximate Reasoning 103 212-226. https://doi.org/10.1016/j.ijar.2018.10.002[23] Wang C. Y. & Wan L. (2018). Type-2 fuzzy implications and fuzzy-valued approximation reasoning. International Journal of Approximate Reasoning 102 108-122. https://doi.org/10.1016/j.ijar.2018.08.004[24] Dutta P. & Limboo B. (2017). Bell-shaped fuzzy soft sets and their application in medical diagnosis. Fuzzy Information and Engineering 9(1) 67-91. https://doi.org/10.1016/j.fiae.2017.03.004
WoS Q
Scopus Q
OpenCitations Citation Count
N/A
Source
Sinop Üniversitesi Fen Bilimleri Dergisi
Volume
6
Issue
1
Start Page
21
End Page
32
Collections
Google Scholar™
