• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.321-330
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• 2006

We introduce the concept of intuitionistic fuzzy weak congruence on a semiring and obtain the relation between intuitionistic fuzzy weak congruence and intuitionistic fuzzy ideal of a semiring. Also we define and investigate intuitionistic fuzzy quotient semiring of a semiring over an intuitionistic fuzzy ideal or over an intuitionistic fuzzy weak congruence.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.149-153
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• 2012

In this paper, we introduce the concept of fuzzy almost strongly (r, s)-semicontinuous mappings on intuitionistic fuzzy topological spaces in $\check{S}$ostak's sense. The relationships among fuzzy strongly (r, s)-semicontinuous, fuzzy almost (r, s)-continuous, fuzzy almost (r, s)-semicontinuous, and fuzzy almost strongly (r, s)-semicontinuous mappings are discussed. The characterization for the fuzzy almost strongly (r, s)-semicontinuous mappings is obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.41-44
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• 2003

First, we investigate some properties of fuzzy compactness. Second, we introduce the concept of fuzzy local compactness in fuzzy topological space and study some of its properties. Finally, we investigate some relations between F-compactness in fuzzy topological spaces and one in fuzzy hyperspaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.87-100
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• 1995

In order to control systems which contain nonlinearities of uncertainties, control strategies must deal with the effects of them. Since most of control methods based on system mathematical models have been mainly developed focused on stability robustness against nonlinearities or uncertainties under the assumption that controlled systems are linear time invariant, they have certain amount of limitations to smartly improve the transient responses of systems disturbed by nonlinearities or uncertainties. In this paper, a nonlinear fuzzy PID control method is suggested which can stably improve the transient responses of systems disturbed by nonlinearities, as well as systems whose mathematical characteristics are not perfectly known. Although the derivation process is based on the design process similar to general fuzzy logic controller, resultant control law has analytical forms with time varying PID gains rather than linguistic forms, so that implementation using common-used versatile microprocessors cna be achieved easily and effectively in real-time control aspect.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.602-609
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• 2003

This paper describes the fault location calculation using neuro-fuzzy systems in combined transmission lines with underground power cables. Neuro-fuzzy systems used in this paper are composed of two parts for fault section and fault location. First, neuro-fuzzy system discriminates the fault section between overhead and underground with normalized detail coefficient obtained by wavelet transform. Normalized detail coefficients of voltage and current in half cycle information are used for the inputs of neuro-fuzzy system. As the result of neuro-fuzzy system for fault section, impedance of selected fault section is calculated and it is used as the inputs of the neuro-fuzzy systems for fault location. Neuro-fuzzy systems for fault location also consist of two parts. One calculates the fault location of overhead, and the other does for underground. Fault section is completely classified and neuro-fuzzy system for fault location calculates the distance from the relaying point. Neuro-fuzzy systems proposed in this paper shows the excellent results of fault section and fault location.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1121-1124
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• 1993

We propose two-degree-of-freedom fuzzy neural network control systems. It has a hierarchical structure of two sets of control knowledge, thus it is easy to extract and refine fuzzy rules before and after the operation has started, and the number of fuzzy rules is reduced. In addition an example application of automatic vehicle operation is reported and its usefulness is shown simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.387-390
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• 1998

In this paper, we suggested a neuro-fuzzy learning algorithm for tuning fuzzy rules, in which a fuzzy system model is of additive-type. Using the method, it is possible to reduce the computation size, since performing the fuzzy inference and tuning the fuzzy rules of each fuzzy subsystem model are independent. Moreover, the efficiency of suggested method is shown by means of a numerical example.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.259-262
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• 2010

Study about fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) were proposed, and analyzed. Unlike fuzzy set, IFSs contains uncertainty named hesistancy, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of ununified entropy definition. By considering different fuzzy entropy definitions, fuzzy entropy is designed and discussed their relation. Similarity measure was also presented and verified its usefulness to evaluate degree of similarity.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.62-68
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• 2001

In this paper, we introduce the concepts of fuzzy r-pre-semiopen and r-pre-semiclosed sets. With them we define fuzzy r-pre-semiinterior and r-pre-semiclosure. We also introduce and investigate the properties of a fuzzy r-pre-semicontinuous map, a fuzzy r-pre-semiopen map and a fuzzy r-pre-semiclosed map. These concepts are generalizations of the Bai Shi-Zhongs fuzzy pre-semicontinuity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.29-33
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• 2001

We propose fuzzy least-squares regression analysis by few error term data and test the slop by fuzzy hypotheses membership function for fuzzy number data with agreement index. Finding the agreement index by area for fuzzy hypotheses membership function and membership function of confidence interval, we obtain the results to acceptance or reject for the test of fuzzy hypotheses.