• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.99-107
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• 2007

In general, the reliabilities of the fuzzy system are represented and analyzed by real numbers between zero and one, fuzzy numbers, intervals of confidence, interval-valued fuzzy sets, vague sets, etc. This paper addresses the method to analyze the reliability of the fuzzy system for the weighted components with the weights reflected on the importance of weighted components in an system. The reliabilities and the weights of the weighted components in a fuzzy numbers and considers the weights of the weighted components in a fuzzy system, therefore, its execution is faster and more flexible than the conventional methods.

• Korean Journal of Mathematics
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• v.4 no.1
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• pp.31-37
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• 1996

We define fuzzy quasicomponents and prove some properties related to fuzzy components.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.281-285
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• 2002

The observation fuzzy data in random effect and balanced designs for one way classification by using a the matrix formulation, we can estimate the fuzzy variance components for the ozone depletion example and test by the agreement index.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.979-985
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• 2006

In the conventional researches, the reliabilities of the fuzzy system are represented and analyzed by real values between zero and one, fuzzy numbers, intervals of confidence, etc. In this paper, we present a method to represent and analyze the reliabilities of the weighted components of the fuzzy system and the weights reflected on their importance based on vague sets defined in the universe of discourse [0, 1]. The vague set is represented as the interval consisted of the truth-membership functions and the false-membership functions, therefore it can allow the reliabilities and the weights of a fuzzy system to represent in a more flexible manner. The proposed method considers the weights of the weighted components in the fuzzy systems, its reliability analysis is more flexible and effective than the conventional methods.

• Journal of the Korea Safety Management & Science
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• v.3 no.2
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• pp.113-121
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• 2001

Today, facilities are composed of many complex components or parts. Because of this characteristics, the frequency of failures is decreasing, but the strength of failures is increasing; therefore, the failure analysis about many complex components or parts was needed. In the former research about Fault Tree Analysis, failure data of similar facilities have been used for forecasting about target system or components, but in case that the system or components for forecasting failure is new or qualitative and quantitative data are given simultaneously, there are many difficulty in using Fault Tree Analysis with this incorrect failure data. Therefore, this paper deal with the Fault Tree Analysis method which be applied with Fuzzy theory in above case. In case that , therefore, if there is no the correct failure data, it is represented a system or components as qualitative variable. subsequently, it converted to the quantitative value using fuzzy theory, and the values used as the value for failure forecast.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.205-214
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• 2018

The reliability test is a crucial step for ensuring robustness of high-cost and complex weapon systems. In this paper, we present a set of quantitative criteria to select critical parts or components in weapon systems for the reliability test, and implement a fuzzy inference system by applying developed criteria to fuzzy theory. We classify the selection criteria of critical parts or components into four fuzzy sets and membership functions. A fuzzy inference rule is proposed based on the AHP (Analytic Hierarchy Process) analysis technique so as to derive a convincing reliability test. The credibility of the fuzzy inference system is confirmed through a case study using actual equipment data exacted from an existent weapon system.

• Journal of Korean Society for Quality Management
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• v.20 no.2
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• pp.44-54
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• 1992

In conventional fault-tree analysis, the failure probabilities of components of a system are treated as exact values in estimating the failure probability of the top event. For the plant layout and systems of the products, however, it is often difficult to evaluate the failure probabilities of components from past occurences, because the environments of the systems change. Furthermore, it might be necessary to consider possible failure of components of the systems even if they have never failed before. In the paper, instead of the probability of failure, we propose the possibility of failure, viz, a fuzzy set defined in probability space. Thus, in this paper based on a fuzzy fault-tree model, the maximum possibility of system failure is determined from the possibility of failure of each component within the system according to the extension principle.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.111-114
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• 1997

Generally, fuzzy models have the capability of dividing input space into several subspaces. compared to liner ones. But hitherto suggested fuzzy modeling algorithms not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem. this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently than conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space. the method of principal component is used. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.58-66
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• 1998

In many situations, it is very important to identify a certain unkown system, it from its input-output data. For this purpose, several system modeling algorithms have been suggested heretofore, and studies regarding the fuzzy modeling based on its nonlinearity get underway as well. Generatlly, fuzzy models have the capability of dividing input space into several subspaces, compared to linear ones. But hitherto subggested fuzzy modeling algorithms do not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem, this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently that conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space, the method of principal component is ued. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1262-1266
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• 2003

Fuzzy sliding mode controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.