Literatura académica sobre el tema "INTUITIONISTIC FUZZY SET"
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Artículos de revistas sobre el tema "INTUITIONISTIC FUZZY SET"
Jeon, Joung Kon, Young Bae Jun y Jin Han Park. "Intuitionistic fuzzy alpha-continuity and intuitionistic fuzzy precontinuity". International Journal of Mathematics and Mathematical Sciences 2005, n.º 19 (2005): 3091–101. http://dx.doi.org/10.1155/ijmms.2005.3091.
Texto completoSzmidt, Eulalia y Janusz Kacprzyk. "A Fuzzy Set Corresponding to an Intuitionistic Fuzzy Set". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, n.º 05 (octubre de 1998): 427–35. http://dx.doi.org/10.1142/s0218488598000343.
Texto completoPratama, Dian. "STRUKTUR IMAGE DAN PRE-IMAGE HOMOMORFISMA PADA TRANSLASI RING FUZZY INTUITIONISTIK". Jurnal Ilmiah Matematika dan Pendidikan Matematika 11, n.º 1 (18 de mayo de 2020): 59. http://dx.doi.org/10.20884/1.jmp.2020.12.1.1937.
Texto completoBashir, Maruah, Abdul Razak Salleh y Shawkat Alkhazaleh. "Possibility Intuitionistic Fuzzy Soft Set". Advances in Decision Sciences 2012 (13 de marzo de 2012): 1–24. http://dx.doi.org/10.1155/2012/404325.
Texto completoMandal, Debabrata. "A Hesitant Intuitionistic Fuzzy Set Approach to Study Ideals of Semirings". International Journal of Fuzzy System Applications 10, n.º 3 (julio de 2021): 1–17. http://dx.doi.org/10.4018/ijfsa.2021070101.
Texto completoRoh, Eun Hwan, Eunsuk Yang y Young Bae Jun. "Intuitionistic Fuzzy Ordered Subalgebras in Ordered BCI-algebras". European Journal of Pure and Applied Mathematics 16, n.º 3 (30 de julio de 2023): 1342–58. http://dx.doi.org/10.29020/nybg.ejpam.v16i3.4832.
Texto completoBalamurugan, Manivannan, Nazek Alessa, Karuppusamy Loganathan y M. Sudheer Kumar. "Bipolar Intuitionistic Fuzzy Soft Ideals of BCK/BCI-Algebras and Its Applications in Decision-Making". Mathematics 11, n.º 21 (28 de octubre de 2023): 4471. http://dx.doi.org/10.3390/math11214471.
Texto completoJana, Chiranjibe y Madhumangal Pal. "Application of Bipolar Intuitionistic Fuzzy Soft Sets in Decision Making Problem". International Journal of Fuzzy System Applications 7, n.º 3 (julio de 2018): 32–55. http://dx.doi.org/10.4018/ijfsa.2018070103.
Texto completoSingh, Shiva Raj, Surendra Singh Gautam y Abhishekh . "An Intuitionistic Fuzzy Soft Set Theoretic Approach to Decision Making Problems". MATEMATIKA 34, n.º 1 (28 de mayo de 2018): 49–58. http://dx.doi.org/10.11113/matematika.v34.n1.890.
Texto completoWijaya, Ongky Denny, Abdul Rouf Alghofari, Noor Hidayat y Mohamad Muslikh. "The Properties of Intuitionistic Anti Fuzzy Module t-norm and t-conorm". CAUCHY 7, n.º 2 (11 de marzo de 2022): 207–19. http://dx.doi.org/10.18860/ca.v7i2.13351.
Texto completoTesis sobre el tema "INTUITIONISTIC FUZZY SET"
KUMARI, RANJEETA y SHIVAM SHARMA. "HIERARCHICAL CLUSTERING OF PICTURE FUZZY RELATION". Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20420.
Texto completoPeng, Jen-pin y 彭仁賓. "The Study of Optimizing Multi-response Problems with Intuitionistic Fuzzy Set". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/79486084912217317899.
Texto completo國立中央大學
機械工程學系
103
The Taguchi method provides an effective framework for improving quality in industry. However, it determines the optimal setting of process parameters according to only single response. For the sake of optimizing multi-response problems, multiple criteria decision making (MCDM) methods have been extensively utilized in recent years. In considering an engineer's opinion in optimizing a multi-response problem, it must be paid to vagueness and hesitancy in revealing his or her perceptions of a fuzzy concept such as 'importance' or 'excellence'. Recently, the notion of intuitionistic fuzzy sets (IFSs) has been found to be more effective than that of fuzzy sets for dealing with vagueness and hesitancy. This thesis focuses on state systems and explores optimization of multi-response problems with IFSs, in which the importance of each response is given by an engineer as IFS. In the proposed methods, the TOPSIS method, VIKOR method and the similarity measure method are proposed for optimizing multi-response problems, where the weight of various responses are assessed in terms of IFSs. This scheme can eliminate the need for complicated intuitionistic fuzzy arithmetic operations and increase the efficiency of solving multi- response optimization problems in intuitionistic fuzzy environments. Two case studies of plasma-enhanced chemical vapor deposition (PECVD) and double-sided surface mount technology electronic assembly operation are used to demonstrate the effectiveness of the proposed methods. These case studies show that the proposed methods are useful schemes to efficiently determine the optimal factor-level combination. The proposed methods differ from previous approaches for optimizing multi-response problems, not only in that the proposed methods use IFSs rather than fuzzy sets, but also in that the calculations are more efficient.
Kabir, Sohag, T. K. Goek, M. Kumar, M. Yazdi y F. Hossain. "A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation". 2019. http://hdl.handle.net/10454/17992.
Texto completoTemporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.
This work was supported in part by the Mobile IOT: Location Aware project (grant no. MMUE/180025) and Indoor Internet of Things (IOT) Tracking Algorithm Development based on Radio Signal Characterisation project (grant no. FRGS/1/2018/TK08/MMU/02/1). This research also received partial support from DEIS H2020 project (grant no. 732242).
Chia-Yi, Chien y 簡嘉毅. "An Improved Cross-Entropy Approach for Pattern Recognition Based on Intuitionistic Fuzzy Set". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/98868305678012283391.
Texto completo國防大學管理學院
運籌管理學系
97
The thesis addresses the issue of information-theoretic discrimination measures for intuitionistic fuzzy sets (IFSs). Although many measures of distance, similarity, dissimilarity, and correlation between IFSs have been proposed, there is no reference regarding information-driven measures used for comparison between sets. In this work, we introduce the concepts of discrimination information and cross-entropy in the intuitionistic fuzzy sets and improve non-probabilistic entropy proposed by Vlachos & Sergiadis (2007) for IFSs. Based on this entropy measure, we add information of hesitation and reveal an intuitive and mathematical connection between the notions of entropy for IFSs in terms of fuzziness and intuitionism. Finally, we demonstrate the applications of the proposed discrimination information measure for pattern recognition, medical diagnosis, and bacteria detection.
Ching-Lin, Lin y 林敬霖. "Kernel Intuitionistic Fuzzy C-Means Clustering Algorithms with Rough Set for Customer Analysis". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/69926836270687990934.
Texto completo龍華科技大學
資訊管理系碩士班
101
Fuzzy C-mean (FCM) algorithms have been widely used in variety of different places. This paper proposes a kernel intuitionistic fuzzy c-means clustering algorithms with rough set (KIFCMRS), and this method is applied to the E-learning data analysis. The rule generation can be divided into two stages for effective rule generation. In the first stage, KIFCM takes advantages of kernel function and intuitionistic fuzzy sets to cluster raw data into similarity groups. In the second stage, the rough set theory is employed to generate rules with different groups. Finally, this paper compared different methods, the first stages comparative KIFCM and the other two methods (KM, FCM), the second stages compare the KIFCMRS and the other two methods (ID3, RS). Comparison with other approaches demonstrate the superior performance of the proposed KIFCMRS.
Libros sobre el tema "INTUITIONISTIC FUZZY SET"
Som, Tanmoy, Oscar Castillo, Anoop Kumar Tiwari y Shivam Shreevastava, eds. Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8566-9.
Texto completoIntuitionistic fuzzy measures: Theory and applications. New York: Nova Science Publishers, 2006.
Buscar texto completoAtanassov, Krassimir T. On Intuitionistic Fuzzy Sets Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Buscar texto completoXiaoqiang, Cai y SpringerLink (Online service), eds. Intuitionistic Fuzzy Information Aggregation: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Buscar texto completoZhi jue mo hu cu cao ji li lun ji ying yong. Beijing: Ke xue chu ban she, 2013.
Buscar texto completoChaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley & Sons, Incorporated, John, 2019.
Buscar texto completoChaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley, 2019.
Buscar texto completoChaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley & Sons, Incorporated, John, 2019.
Buscar texto completoChaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley & Sons, Limited, John, 2019.
Buscar texto completoTiwari, Anoop Kumar, Shivam Shreevastava, Oscar Castillo y Tanmoy Som. Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling: Theory and Applications. Springer, 2023.
Buscar texto completoCapítulos de libros sobre el tema "INTUITIONISTIC FUZZY SET"
Li, Deng-Feng. "Intuitionistic Fuzzy Set Theories". En Decision and Game Theory in Management With Intuitionistic Fuzzy Sets, 1–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40712-3_1.
Texto completoWu, Le-tao y Xue-hai Yuan. "Intuitionistic Fuzzy Rough Set Based on the Cut Sets of Intuitionistic Fuzzy Set". En Advances in Intelligent Systems and Computing, 37–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66514-6_4.
Texto completoGupta, Krishna Kumar y Sanjay Kumar. "Probabilistic Intuitionistic Fuzzy Set Based Intuitionistic Fuzzy Time Series Forecasting Method". En Mathematical Modelling and Scientific Computing with Applications, 315–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1338-1_23.
Texto completoZhang, Yanqin y Xibei Yang. "An Intuitionistic Fuzzy Dominance–Based Rough Set". En Bio-Inspired Computing and Applications, 665–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_88.
Texto completoCornelis, Chris y Etienne Kerre. "Inclusion Measures in Intuitionistic Fuzzy Set Theory". En Lecture Notes in Computer Science, 345–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45062-7_28.
Texto completoKahraman, Cengiz, Alexander Bozhenyuk y Margarita Knyazeva. "Internally Stable Set in Intuitionistic Fuzzy Graph". En Lecture Notes in Networks and Systems, 566–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09173-5_65.
Texto completoAtanassov, Krassimir T. "On the Intuitionistic Fuzzy Implications and Negations. Part 1". En 35 Years of Fuzzy Set Theory, 19–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16629-7_2.
Texto completoGupta, Ananya y Shahin Ara Begum. "Fuzzy Rough Set-Based Feature Selection for Text Categorization". En Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling, 65–85. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8566-9_4.
Texto completoBisht, Kamlesh, Dheeraj Kumar Joshi y Sanjay Kumar. "Dual Hesitant Fuzzy Set-Based Intuitionistic Fuzzy Time Series Forecasting". En Advances in Intelligent Systems and Computing, 317–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7386-1_28.
Texto completoChaira, Tamalika y Tridib Chaira. "Intuitionistic Fuzzy Set: Application to Medical Image Segmentation". En Computational Intelligence in Medical Informatics, 51–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75767-2_3.
Texto completoActas de conferencias sobre el tema "INTUITIONISTIC FUZZY SET"
Xu, Yong-jie, Yong-kan Sun y Deng-feng Li. "Intuitionistic Fuzzy Soft Set". En 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473444.
Texto completoLu, Yanli, Yingjie Lei y Yang Lei. "Intuitionistic fuzzy rough set based on intuitionistic similarity relation". En 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597422.
Texto completoXu, Yon-Hong y Wei-Zhi Wu. "On intuitionistic fuzzy rough set algebras". En 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580541.
Texto completoNair, Premchand S. "Consolidation operator for Intuitionistic Fuzzy Set". En NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2009. http://dx.doi.org/10.1109/nafips.2009.5156483.
Texto completoZenian, Suzelawati, Tahir Ahmad y Amidora Idris. "Intuitionistic fuzzy set: FEEG image representation". En PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136486.
Texto completoKumar, Deepak y S. B. Singh. "Evaluating fuzzy reliability using rough intuitionistic fuzzy set". En 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). IEEE, 2014. http://dx.doi.org/10.1109/cipech.2014.7019037.
Texto completoKhatibi, Vahid y Gholam Ali Montazer. "Intuitionistic fuzzy set application in bacteria recognition". En 2009 14th International CSI Computer Conference (CSICC 2009) (Postponed from July 2009). IEEE, 2009. http://dx.doi.org/10.1109/csicc.2009.5349609.
Texto completoChaira, Tamalika. "Medical image enhancement using intuitionistic fuzzy set". En 2012 1st International Conference on Recent Advances in Information Technology (RAIT). IEEE, 2012. http://dx.doi.org/10.1109/rait.2012.6194479.
Texto completoKaushal, Meenakshi, Rinki Solanki, Q. M. Danish Lohani y Pranab K. Muhuri. "A Novel Intuitionistic Fuzzy Set Generator with Application to Clustering". En 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491602.
Texto completoXU, Yong-jie. "Some New Operations on Triangular Fuzzy Number Intuitionistic Fuzzy Set". En 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8833267.
Texto completoInformes sobre el tema "INTUITIONISTIC FUZZY SET"
Atanasso, Krassimir. Elliptic Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, junio de 2021. http://dx.doi.org/10.7546/crabs.2021.06.02.
Texto completoIsmaili, Shpend y Stefka Fidanova. Application of Intuitionistic Fuzzy Sets on Agent Based Modelling. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, junio de 2018. http://dx.doi.org/10.7546/crabs.2018.06.12.
Texto completoAtanassov, Krassimir. A New Modal Type Operator over Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, agosto de 2020. http://dx.doi.org/10.7546/crabs.2020.08.01.
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