نتایج جستجو برای: trapezoidal and triangular fuzzy numbers

تعداد نتایج: 16870558  

Journal: :Communications in Mathematics and Applications 2022

This paper is focused on arithmetic operations fuzzy and intuitionistic numbers to solve the unconstrained optimization problems with triangular trapezoidal, number coefficients. The optimal solution obtained by Newton’s method, MATLAB outputs are also provided illustrative examples. method proposed in this research work has been compared existing method.

Journal: :Soft Computing 2021

Abstract We define $$2n+1$$ 2 n + 1 and 2 n fuzzy numbers, which generalize triangular trapezoidal respectively. Then, we extend the preference relation relative to rank numbers. When data is representable in terms of number, FMCDM (fuz...

In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...

S. Mizuno S.H. Nasseri,

Ranking fuzzy numbers plays a very important role in linguistic decision making and other fuzzy application systems. In spite of many ranking methods, no one can rank fuzzy numbers with human intuition consistently in all cases. Shortcoming are found in some of the convenient methods for ranking triangular fuzzy numbers such as the coefficient of variation (CV index), distance between fuzzy set...

Journal: :journal of linear and topological algebra (jlta) 0
s. p mondal department of mathematics, national institute of technology, agartala, jirania-799046, tripura, india t. k roy department of mathematics, indian institute of engineering science and technology, shibpur, howrah-711103, west bengal, india

in this paper the solution of a second order linear di erential equations with intu-itionistic fuzzy boundary value is described. it is discussed for two di erent cases: coecientis positive crisp number and coecient is negative crisp number. here fuzzy numbers aretaken as generalized trapezoidal intutionistic fuzzy numbers (gtrifns). further a numericalexample is illustrated.

Trapezoidal intuitionistic fuzzy numbers (TrIFNs) express abundant and flexible information in a suitable manner and  are very useful to depict the decision information in the procedure of decision making. In this paper, some new aggregation operators, such as, trapezoidal intuitionistic fuzzy weighted power harmonic mean (TrIFWPHM) operator, trapezoidal intuitionistic fuzzy ordered weighted po...

Journal: :Fuzzy Sets and Systems 1997
Ronald E. Giachetti Robert E. Young

Direct implementation of extended arithmetic operators on fuzzy numbers is computationally complex. Implementation of the extension principle is equivalent to solving a nonlinear programming problem. To overcome this difficulty many applications limit the membership functions to certain shapes, usually either triangular fuzzy numbers (TFN) or trapezoidal fuzzy numbers (TrFN). Then calculation o...

Journal: :iranian journal of optimization 2010
a. kumar a. bansal a. neetu

different methods have been proposed for finding the non-negative solution of fully fuzzy linear system (ffls) i.e. fuzzy linear system with fuzzy coefficients involving fuzzy variables. to the best of our knowledge, there is no method in the literature for finding the non-negative solution of a ffls without any restriction on the coefficient matrix. in this paper a new computational method is ...

2012
Manju Pandey Nilay Khare

In recent work authors have proposed four new aggregation operators for triangular and trapezoidal fuzzy numbers based on means of apex angles [1][2][3][4]. Subsequently authors have proposed [5] a new aggregation operator for TFNs based on the arithmetic mean of slopes of the Land Rmembership lines. In this paper the work is extended and a new aggregation operator for TFNs is proposed in which...

Journal: :Int. J. Computational Intelligence Systems 2010
Sevil Sentürk

The fuzzy regression control chart is a functional technique to evaluate the process in which the average has a trend and the data represents a linguistic or approximate value. In this study, the theoretical structure of the “α-level fuzzy midrange for α-cut fuzzy X ~ -regression control chart” is proposed for triangular (TFN) and trapezoidal (TrFN) membership functions. In addition, the real w...

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