• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.113-120
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• 2012

In this paper we study various properties of the fuzzy linear maps over the fuzzy quotient spaces. In particular we obtain some exact sequences of the fuzzy linear maps over the fuzzy quotient spaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.268-271
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• 2008

In this paper, we introduce the notions of a fuzzy convergence of sequences, fuzzy Cauchy sequence and the related fuzzy completeness on a fuzzy normed linear space. And we investigate some properties relative to fuzzy normed linear spaces. In particular, we prove an equivalent conditions that a fuzzy norm defined on a ordinary normed linear space is fuzzy complete.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.511-513
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• 2005

We investigate some situations in connection with two exact sequences of fuzzy linear maps.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.232-239
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• 2006

This paper is for applying interactive fuzzy linear programming for the problem of product mix planning, which is one of the aggregate planning problem. We developed a modified algorithm, which has two-phase approach for interactive fuzzy linear programming to get a better solution. Adding two-phase method, we expect to obtain not only the highest membership degree, but also a better utilization of each constrained resource.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.499-505
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• 2006

Regularlization approach to regression can be easily found in Statistics and Information Science literature. The technique of regularlization was introduced as a way of controlling the smoothness properties of regression function. In this paper, we have presented a new method to evaluate linear and non-linear fuzzy regression model based on Tanaka's model using the idea of regularlization technique. Especially this method is a very attractive approach to model non -linear fuzzy data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.191-194
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• 2000

In this paper, an output tracking control technique of discrete-time Takagi-Sugeno (TS) fuzzy systems is developed. The TS fuzzy system is represented as an uncertain multiple linear system. The tracking problem of TS fuzzy system is converted into the stabilization problem of a uncertain multiple linear system. A sufficient condition for asymptotic tracking is obtained in terms of linear matrix inequalities (LMI). A design example is illustrated to show the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.260-263
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• 2000

In this paper, a tracking control technique of Takagi-Sugeno(TS) fuzzy systems with parametric uncertainties is developed. The uncertain TS fuzzy system is represented as an uncertain multiple linear system. The tracking problem of TS fuzzy system is converted into the regulation problem of a multiple linear system. A sufficient condition for robust tracking is obtained in terms of linear matrix inequalities(LMI). A Design example is illustrated to show the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.696-700
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• 1998

Fuzzy random variables with piecewise linear membership functions are introduced from a practical viewpoint. The estimation of the expected values of these fuzzy random variables is also discussed and statistical application is denonstratied by using a real data set.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.506-511
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• 2011

In this paper we investigate some situations in connection with two exact sequences of fuzzy linear maps. Also we obtain a generalization of the work [Theorem4] of Pan [5], and we study the analogies of The Four Lemma and The Five Lemma of homological algebra. Finally we obtain a special exact sequence.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.324-328
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• 2007

An analytical framework for fuzzy modeling and control of nonlinear systems using a set of linear models is presented. Fuzzy clustering is applied on the aerodynamic coefficients of a missile to obtain an optimal number of rules in a Tagaki-Sugeno fuzzy rule-set. Next, the obtained membership functions and rule-sets are applied to a set of linear optimal controllers towards extraction of a global controller. Reported simulations demonstrate the performance, stability, and robustness of the controller.