Results 221 to 230 of about 41,990 (276)
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ARITHMETIC MEAN BASED COMPENSATORY FUZZY LOGIC
International Journal of Computational Intelligence and Applications, 2011Fuzzy Logic is a multi-valued logic model based on fuzzy set theory, which may be considered as an extension of Boolean Logic. One of the fields of this theory is the Compensatory Fuzzy Logic, based on the removal of some axioms in order to achieve a sensitive and idempotent multi-valued system.
Bouchet, Agustina +4 more
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Fuzzy Logic and Arithmetical Hierarchy III
Studia Logica, 1997[For Part I see Fuzzy Sets Syst. 73, No. 3, 359-363 (1995; Zbl 0857.03011).] Fuzzy logic RQL here means an extension of Łukasiewicz's infinite-valued first-order logic by graded notions of provability and of consequence together with (truth degree) constants for all rationals of the truth degree set \([0,1]\).
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2016
The paper presents notion of horizontal membership function (HMF) and based on it fuzzy, relative distance measure (fuzzy RDM) arithmetic that is compared with standard fuzzy arithmetic (SF arithmetic). Fuzzy RDM-arithmetic possess such mathematical properties which allow for achieving complete fuzzy solution sets of problems, whereas SF-arithmetic, in
Andrzej Piegat, Marek Landowski
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The paper presents notion of horizontal membership function (HMF) and based on it fuzzy, relative distance measure (fuzzy RDM) arithmetic that is compared with standard fuzzy arithmetic (SF arithmetic). Fuzzy RDM-arithmetic possess such mathematical properties which allow for achieving complete fuzzy solution sets of problems, whereas SF-arithmetic, in
Andrzej Piegat, Marek Landowski
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Fuzzy Arithmetic-Based Interpolative Reasoning
IFAC Proceedings Volumes, 1997Abstract FAIR - Fuzzy Arithmetic based Interpolative Reasoning is presented. Linguistic rules of the Mamdani type with fuzzy numbers as consequents are used in an inference mechanism similar to that of the Takagi-Sugeno model. The inference result is a weighted sum of fuzzy numbers calculated by means of the extension principle.
M. Setnes +3 more
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Fuzzy Set Transformation and Fuzzy Arithmetic
2020The chapter elaborates on the ways of transforming fuzzy sets through functions: one of the fundamental concepts of processing fuzzy sets. The extension principle is discussed in detail. Two key ways of mapping fuzzy sets are discussed: the one based on the representation theorem (which directly links to the mapping realized in interval analysis) and ...
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Why Multidimensional Fuzzy Arithmetic?
2018In the paper authors try to convince readers that application of multidimensional fuzzy arithmetic (MFAr) is useful because this arithmetic delivers more precise solutions of uncertain problems than low-dimensional fuzzy arithmetic, which is mostly used at present.
Andrzej Piegat, Marek Landowski
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Interpolative model for fuzzy arithmetic
Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002Standard model of fuzzy computations is based on extension principle. It is known to work well, in practice, only for continuous fuzzy numbers, while producing unintuitive results when one or more arguments are discrete. It is also computationally cumbersome for all but linear operations. Another model was proposed for trapezoidal numbers only.
Ramer, Arthur +4 more
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Fuzzy Sets and Systems, 1999
The fuzzy average and the fuzzy arithmetic mean are represented in this paper. It has been proved that with a theorem the fuzzy arithmetic mean converges to the normal arithmetic mean. It is shown how to use the fuzzy average, which is compared with fuzzy control rules with an example.
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The fuzzy average and the fuzzy arithmetic mean are represented in this paper. It has been proved that with a theorem the fuzzy arithmetic mean converges to the normal arithmetic mean. It is shown how to use the fuzzy average, which is compared with fuzzy control rules with an example.
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Real-Time Constrained Fuzzy Arithmetic
IEEE Transactions on Fuzzy Systems, 2009Klir introduced constrained fuzzy arithmetic (CFA) as a solution to the unnecessary precision loss when dealing with fuzzy quantities that represent linguistic variables. Since then, some attempts have been made to make CFA efficient, but none of these solutions is suitable for real-time applications.
P. Victor +3 more
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Complex fuzzy arithmetic aggregation operators
Journal of Intelligent & Fuzzy Systems, 2019A complex fuzzy set, characterized by complex-valued membership functions, is a generalization of a fuzzy set. In this paper, we present complex fuzzy arithmetic aggregation (CFAA) operators, complex fuzzy weighted arithmetic aggregation (CFWAA) operators.
Bi, Lvqing +3 more
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