• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.513-520
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• 2005

We calculate the normal fuzzy probability for trigonometric fuzzy numbers defined by trigonometric functions. And we study the normal probability for some operations of two trigonometric fuzzy numbers. Furthermore, we calculate the normal fuzzy probability for some fuzzy numbers generated by operations.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.98-103
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• 2005

In this paper, we consider interval number-valued random variables and fuzzy number-valued random variables and discuss Choquet integrals of them. Using these properties, we define the Choquet expected value of fuzzy number-valued random variables which is a natural generalization of the Lebesgue expected value of fuzzy random variables. Furthermore, we discuss some application of them.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.279-284
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• 1995

In CAI, it is very important to evaluate the grade of understanding which students reach about the scope of problem which students are studying. In this paper, to find out students' learning achievement, we make students reply to test which the system presents and then lead evaluation result using fuzzy number about answer result. Besides, we define the degree of prior knowledge of studentsd as conditioned fuzzy number and use existing fuzzy accuracy production function begore the stage of using fuzzy number, Next, we apply conditioned fuzzy number to accuracy degree of answer produces by this function. Through this, we come to the conclusion that evaluation result as to the same answer result is changed according to the degree of prior knowledge about the scope which students are studying.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.18-21
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• 2003

We propose some properties of fuzzy conditional expectation of hybrid number the addition of fuzzy number and random variable using Cartesian product distance for ${\alpha}$-level sets.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.831-846
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• 2011

There are several methods, in the literature, for finding the fuzzy optimal solution of fully fuzzy transportation problems (transportation problems in which all the parameters are represented by fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings, a new method (based on fuzzy linear programming formulation) is proposed to find the fuzzy optimal solution of unbalanced fuzzy transportation problems with a new representation of trapezoidal fuzzy numbers. The advantages of the proposed method over existing method are discussed. Also, it is shown that it is better to use the proposed representation of trapezoidal fuzzy numbers instead of existing representation of trapezoidal fuzzy numbers for finding the fuzzy optimal solution of fuzzy transportation problems. To illustrate the proposed method a fuzzy transportation problem (FTP) is solved by using the proposed method and the obtained results are discussed. The proposed method is easy to understand and to apply for finding the fuzzy optimal solution of fuzzy transportation problems occurring in real life situations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.365-369
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• 2006

Interval-valued fuzzy sets were suggested for the first time by Gorzalczang(1983) and Turken(1986). Based on this, Wang and Li extended their operations on interval-valued fuzzy numbers. Recently, Hong(2002) generalized results of Wang and Li and extended to interval-valued fuzzy sets with Riemann integral. Using interval-valued Choquet integrals with respect to a fuzzy measure instead of Riemann integrals with respect to a classical measure, we studied some characterizations of interval-valued Choquet distance(2005). In this paper, we define Choquet distance measure for fuzzy number-valued fuzzy numbers and investigate some algebraic properties of them.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.219-223
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• 2001

In this paper, we proposed the intelligent navigation agent model for successive electronic commerce management. For allowing intelligence, we used conditional probability and trapezoidal fuzzy number. Our goal of study is make an intelligent automatic navigation agent model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.41-45
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• 2002

From the paper which it sees a strong random number generator it uses a fuzzy set from 16 method of the statistical test which is a cryptograph random number test it verifies. 16 statistical test of NIST extends in crptograph and engineering whole it is a scale which is important distinguishes the distinction incapable characterstic of the random numbers which are used. To try introduce a fuzzy set the possibility of having a more strong randomness in order to be, it strengthens the function of the random number generator.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.