• Journal of the Chungcheong Mathematical Society
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• v.24 no.2
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• pp.253-266
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• 2011

We would like to generalize about trapezoidal fuzzy set and to calculate four operations based on the Zadeh's extension principle for two generalized trapezoidal fuzzy sets. And we roll up triangular fuzzy numbers and generalized triangular fuzzy sets into it. Since triangular fuzzy numbers and generalized triangular fuzzy sets are generalized trapezoidal fuzzy sets, we need no more the separate painstaking calculations of addition, subtraction, multiplication and division for two such kinds once the operations are done for generalized trapezoidal fuzzy sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.212-217
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• 2012

A fuzzy set $A$ defined on a probability space ${\Omega}$, $\mathfrak{F}$, $P$ is called a fuzzy event. Zadeh defines the probability of the fuzzy event $A$ using the probability $P$. We define the generalized triangular fuzzy set and apply the extended algebraic operations to these fuzzy sets. A generalized triangular fuzzy set is symmetric and may not have value 1. For two generalized triangular fuzzy sets $A$ and $B$, $A(+)B$ and $A(-)B$ become generalized trapezoidal fuzzy sets, but $A({\cdot})B$ and $A(/)B$ need not to be a generalized triangular fuzzy set or a generalized trapezoidal fuzzy set. We define the normal fuzzy probability on $\mathbb{R}$ using the normal distribution. And we calculate the normal fuzzy probability for generalized triangular fuzzy sets.

• Journal of the Chungcheong Mathematical Society
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• v.22 no.2
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• pp.161-170
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• 2009

For various fuzzy numbers, many operations have been calculated. We generalize about triangular fuzzy number and calculate four operations based on the Zadeh's extension principle, addition A(+)B, subtraction A(-)B, multiplication A(${\cdot}$)B and division A(/)B for two generalized triangular fuzzy sets.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.445-449
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• 2005

Yen et al. [Fuzzy Sets and Systems 106 (1999) 167-177] calculated the fuzzy membership function for the output to find the non-symmetric triangular fuzzy number coefficients of a linear regression model for all given input-output data sets. In this note, we show that the result they obtained in their paper is invalid.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.2
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• pp.297-308
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• 2013

We define one-sided quadrangular fuzzy sets, a left quadrangular fuzzy set and a right quadrangular fuzzy set. And then we generalize the results of addition, subtraction, multiplication, and division based on the Zadeh's extension principle for two one-sided quadrangular fuzzy sets. In addtion, we find the condition that the result of addition or subtraction for two one-sided quadrangular fuzzy sets becomes a triangular fuzzy number.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.2
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• pp.277-286
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• 2014

We define the pentagonal fuzzy sets and generalize the results of addition, subtraction, multiplication, and division based on the Zadeh's extension principle for two pentagonal fuzzy sets. In addtion, we find the condition that the result of addition or subtraction for two pentagonal fuzzy sets becomes a triangular fuzzy number and give some example.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.445-450
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• 2008

In order to analyze the reliabilities of the fuzzy systems, the reliabilities of the components in the fuzzy systems are represented by real values between zero and one, fuzzy numbers, intervals of confidence, vague sets, interval valued fuzzy sets, etc in the conventional researches. In this paper, we propose a method to represent and analyze the reliabilities of the fuzzy systems based on the interval valued vague sets defined in the universe of discourse [0, 1]. In the interval valued vague sets, the upper bounds and the lower bounds of the conventional vague sets[12, 14] are represented as the intervals. Therefore, it can allow the reliabilities of a fuzzy system to represent and analyze in a more flexible manner. Because the proposed method uses the simplified arithmetic operations of the fuzzy triangular numbers rather than the complicated of the fuzzy trapezoidal numbers mentioned by Kumar[14], the execution of the proposed method is faster than the one.

• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.69-81
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• 1997

In this paper, first we propose an architecture of fuzzy neural networks with triangular fuzzy weights. The proposed fuzzy neural network can handle fuzzy input vectors. In both cases, outputs from the fuzzy network are fuzzy vectors. The input-output relation of each unit of the fuzzy neural network is defined by the extention principle of Zadeh. Also we define a cost function for the level sets(i. e., $\alpha$-cuts)of fuzzy outputs and fuzzy targets. Then we derive a learning algorithm from the cost function for adjusting three parameters of each triangular fuzzy weight. Finally, we illustrate our a, pp.oach by computer simulation examples.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.328-331
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• 2001

In Cretu [Fuzzy Sets and Systems 120(2001) 371-383], triangular norms and their hierarchy are investigated. In this paper, we give new proofs which are significantly shorter than those given in Cretu, applying a known result which involves only one argument of one-place rather than two place arguments by Klement et al. [FSS 86(1997) 189-195]

• Coupled systems mechanics
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• v.2 no.3
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• pp.255-269
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• 2013

This paper targets to investigate the solution of fuzzy quadratic Riccati differential equations with various types of fuzzy environment using Homotopy Perturbation Method (HPM). Fuzzy convex normalized sets are used for the fuzzy parameter and variables. Obtained results are depicted in term of plots to show the efficiency of the proposed method.