• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1305-1308
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• 1993

A novel digital signal processing technique based on fuzzy rules is proposed for estimating nonstationary signals, such as image signals, contaminated with additive random noises. In this filter, fuzzy rules are utilized to set the filter parameters, taking the local characteristics of the signal into consideration. The introduction of the fuzzy rules is effective, since the rules to set the filter parameters is usually expressed ambiguously. Computer simulations verify its high performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.39-43
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• 2000

In this paper, we show that the fuzzy sample mean is a strong consistent estimator for the expectation of a fuzzy random set taking values in the space F(R$\^$/p) of upper semicontinuous convex fuzzy subsets of R$\^$/p with compact support.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.180-184
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• 1997

This paper presents the fuzzy neural network which utilizes a fuzzified Kohonen learning uses a fuzzy membership value, a function of the iteration, and a intra-membership value instead of a learning rate. The IRIS data set if used to test the fuzzy neural network. The test result shows the performance of the fuzzy neural network depends on k and the vigilance parameter T.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.9-12
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• 2000

In this paper, we are interested in counting the number of elements of a type two fuzzy set. Using concepts of type-two fuzzy sets, we can obtain some properties of these concepts and some results of possibility of type-two fuzzy sets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.196-199
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• 2005

Recently, many types of set-valued fuzzy integrals are studied by many authors. In this paper, we consider various types of convergence theorems of Choquet integrals of interval-valued function with respect to an autocontinuous fuzzy measure.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.191-194
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• 2007

In this paper, we introduce the concepts of fuzzy (r,s)-pre-semiopen sets and fuzzy (r,s)-pre-semicontinuous mappings on intuitionistic fuzzy topological spaces in ${\v{S}}ostak's$ sense. The concepts of fuzzy (r,s)-pre-semiinterior, fuzzy (r,s)-pre-semiclosure, fuzzy (r,s)-pre-semineighborhood, and fuzzy (r,s)-quasi-pre-semineighborhood are given, and several properties of these concepts are discussed. Using these concepts, the characterizations for the fuzzy (r,s)-pre-semicontinuous mappings are obtained. Also, we introduce the notions of fuzzy (r,s)-presemiopen and fuzzy (r,s)-pre-semiclosed mappings on intuitionistic fuzzy topologica spaces in ${\v{S}}ostak's$ sense, and then we investigate some of their characteristic properties.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.7-16
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• 2007

First, we investigate fuzzy equivalence relations on a set X in the sense of Youssef and Dib. Second, we discuss fuzzy congruences generated by a given fuzzy relation on a fuzzy groupoid. In particular, we obtain the characterizations of ${\rho}\;o\;{\sigma}{\in}$ FC(S) for any two fuzzy congruences ${\rho}\;and\;{\sigma}$ on a fuzzy groupoid ($S,{\odot}$). Finally, we study the lattice of fuzzy equivalence relations (congruences) on a fuzzy semigroup and give certain lattice theoretic properties.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.927-935
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• 2005

The certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions can be represented by intervals, such as vague numbers between zero and one based on vague sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner[18]. we are also proposed an efficient algorithm to perform vague set reasoning automatically. This vague set reasoning algorithm allows the rule-based systems to perform reasoning in a more flexible and more efficient.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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• pp.3-33
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• 1992

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown ill this paper that the performance of fuzzy control systems call be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate all optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.

• International Journal of Reliability and Applications
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• v.12 no.2
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• pp.79-94
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• 2011

Redundancy-reliability allocation problems in multi-stage series-parallel systems are addressed in this study. Fuzzy programming techniques are proposed for finding satisfactory solutions. First, a multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost. Due to the nature of uncertainty in the problem, the fuzzy set theory and technique are used to convert the deterministic multi-objective programming model into a fuzzy nonlinear programming problem. A heuristic method is developed to get satisfactory solutions for the fuzzy nonlinear programming problem. A Pareto optimal solution is found with maximal degree of satisfaction from the interception area of fuzzy sets. A case study that is related to the electronic control unit installed on aircraft engine over-speed protection system is used to implement the developed approach. Results suggest that the developed fuzzy multi-objective programming model can effectively resolve the fuzzy and uncertain problem when design goals and constraints are not clearly confirmed at the initial conceptual design phase.