Results 101 to 110 of about 8,801 (134)
Quantum-like representation of neuronal networks' activity: modeling "mental entanglement". [PDF]
Khrennikov A, Yamada M.
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Characterizations of uni-nullnorms with continuous Archimedean underlying t-norms and t-conorms
Fuzzy Sets and Systems, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Feng Sun, Xue-ping Wang, Xiao-bing Qu
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Mathematica Slovaca, 2023
ABSTRACT The issue of distributivity of aggregation operators is crucial for many different areas such as fuzzy sets and fuzzy logic, pseudo-analysis and measure theory, and particulary in the decision making theory. The problem of distributivity of an operator form a special class of uni-nullnorms over a general uninorm is being ...
Jočić, Dragan, Štajner-Papuga, Ivana
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ABSTRACT The issue of distributivity of aggregation operators is crucial for many different areas such as fuzzy sets and fuzzy logic, pseudo-analysis and measure theory, and particulary in the decision making theory. The problem of distributivity of an operator form a special class of uni-nullnorms over a general uninorm is being ...
Jočić, Dragan, Štajner-Papuga, Ivana
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IEEE Transactions on Fuzzy Systems, 2021
Yager's q -rung orthopair fuzzy set is a generalization of fuzzy sets, whose prominent feature is that the q th power sum of the membership and the nonmembership degrees is equal to or less than one, and we call its core, an ordered pair, q -rung orthopair fuzzy number ( q -ROFN).
Zhenghai Ai +3 more
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Yager's q -rung orthopair fuzzy set is a generalization of fuzzy sets, whose prominent feature is that the q th power sum of the membership and the nonmembership degrees is equal to or less than one, and we call its core, an ordered pair, q -rung orthopair fuzzy number ( q -ROFN).
Zhenghai Ai +3 more
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Continuous Archimedean t-norms and their bounds
Fuzzy Sets and Systems, 2001In this interesting paper the authors study upper and lower bounds in the class of continuous Archimedean t-norms. Using additive generators and the class of subadditive functions, the authors describe (in a constructive way) how to construct these bounds. Extensions and some applications are presented.
Marko, V., Mesiar, R.
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Reconstruction of Additive Generators from Partial Derivatives of Continuous Archimedean t-Norms
2010 40th IEEE International Symposium on Multiple-Valued Logic, 2010The paper shows a direct correspondence between the first partial derivatives of a continuous Archimedean triangular norm and the first derivatives of its additive generator. An explicit formula for the additive generator is obtained. Application of the result is demonstrated on the problem of convex combinations of strict triangular norms.
Navara, M. +2 more
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Fuzzy implications derived from additive generators of continuous Archimedean t-norms
International Journal of Intelligent Systems, 2012One class of fuzzy implications is introduced by means of the additive generators of continuous Archimedean t-norms. Basic properties of these implications are discussed. These implications are shown to be different from the known (S,N)-, R-, QL-, and Yager's f- and g-implications. Several functional equations with fuzzy implications are investigated. ©
Hua-Wen Liu, Zhen-Bo Li
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Journal of Intelligent & Fuzzy Systems, 2019
The conditional distributivity, also called restricted distributivity, is crucial for many different areas such as utility theory and integral theory. This is the because it weakens distributivity on the domain. This paper is focused on and fully characterizes the conditional distributivity for a uni-nullnorm with continuous and Archimedean underlying ...
Wang, Gang, Qin, Feng, Li, Wen-Huang
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The conditional distributivity, also called restricted distributivity, is crucial for many different areas such as utility theory and integral theory. This is the because it weakens distributivity on the domain. This paper is focused on and fully characterizes the conditional distributivity for a uni-nullnorm with continuous and Archimedean underlying ...
Wang, Gang, Qin, Feng, Li, Wen-Huang
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2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), 2011
In order to avoid combinatorial rule explosion in fuzzy reasoning, in this work we explore the distributive equation of implication I(T(x, y), z) = S(I(x, z), I(y, z)). In detail, by means of the sections of I, we give out the sufficient and necessary conditions of solutions for the distributive equation of implication I(T (x, y), z) = S(I(x, z), I(y ...
Feng Qin, Ping-Chong Yang
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In order to avoid combinatorial rule explosion in fuzzy reasoning, in this work we explore the distributive equation of implication I(T(x, y), z) = S(I(x, z), I(y, z)). In detail, by means of the sections of I, we give out the sufficient and necessary conditions of solutions for the distributive equation of implication I(T (x, y), z) = S(I(x, z), I(y ...
Feng Qin, Ping-Chong Yang
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