• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.712-717
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• 2007

This paper is concerned with the problem of state feedback control of continuous-time nonlinear Markovian jump systems, which are represented by Takagi-Sugeno fuzzy models. A new method for designing state feedback stabilizing controllers is presented in terms of solvability of a set of linear matrix inequalities (LMIs), and it is shown that the new design method provides better or at least the same results of the existing method in the literature. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.419-422
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• 2005

In this note, the Takagi-Sugeno (T-S) fuzzy-model-based state estimator using standard Kalman filter theory is investigated. In that case, the dynamic system model is represented the T-S fuzzy model with the fuzzy state estimation. The steady state solutions can be found for proposed modeling method and dynamic system for maneuvering targets can be approximated as locally linear system. And then, modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.845-848
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• 1993

This paper proposes a design method of fuzzy phase-lead compensator and its self-learning by neural network. The main feature of the fuzzy phase-lead compensator is to have parameters for effectively compensating phase characteristics of control systems. An important theorem which is related to phase-lead compensation is derived by introducing concept of frequency characteristics. We propose a design procedure of fuzzy phase-lead compensators for linear controlled objects. Furthermore, we realize a neuro-fuzzy compensator for unknown or nonlinear controlled objects by using Widrow-Hoff learning rule.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.59-64
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• 2011

In 2001, the notion of a fuzzy poset defined on a set X via a triplet (L, G, I) of functions with domain X ${\times}$ X and range [0, 1] satisfying a special condition L+G+I = 1 is introduced by J. Negger and Hee Sik Kim, where L is the 'less than' function, G is the 'greater than' function, and I is the 'incomparable to' function. Using this approach, we are able to define a special class of fuzzy posets, and define the 'skeleton' of a fuzzy poset in view of major relation. In this sense, we define the linear discrepancy of a fuzzy poset of size n as the minimum value of all maximum of I(x, y)${\mid}$f(x)-f(y)${\mid}$ for f ${\in}$ F and x, y ${\in}$ X with I(x, y) > $\frac{1}{2}$, where F is the set of all injective order-preserving maps from the fuzzy poset to the set of positive integers. We first show that the definition is well-defined. Then, it is shown that the optimality appears at the same injective order-preserving maps in both cases of a fuzzy poset and its skeleton if the linear discrepancy of a skeleton of a fuzzy poset is 1.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.773-776
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• 1993

A design method for linear combiner type filters, based on a fuzzy variant of the usual design method, is introduced and analyzed. Design results are exemplified.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.324-329
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• 2005

In this paper, a robust $H\infty$ stabilization problem to a uncertain discrete-time nonlinear systems with time-delay via fuzzy static output feedback is investigated. The Takagj-Sugeno (T-S) fuzzy model is employed to represent an uncertain nonlinear system with time-delayed state. Then, the parallel distributed compensation technique is used for designing of the robust fuzzy controller. Using a single Lyapunov function, the globally asymptotic stability and disturbance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust $H\infty$ controllers are given in terms of linear matrix inequalities via similarity transform and congruence transform technique. We have shown the effectiveness and feasibility of the proposed method through the simulation.

• Journal of Korean Port Research
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• v.13 no.1
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• pp.79-86
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• 1999

This paper deals with the evaluation problem of complex systems by introducing a fuzzy approach. The authors are functionally supposing a hierarchical structure model of a complex system and give light on the following problems. First for the purpose of clarifying the characteristics of measures the property and differences between two method such as linear and fuzzy viewpoint are discussed through two level-down evaluation process. Second the integrated evaluation process which keeps reversibility between hierarchical levels is discussed and obtained some necessary conditions for reversibility of fuzzy evaluation. From these results it is expected that the fuzzy approach overcomes partly the limitation of reductionism at the hierarchical evaluation of complex systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.175-178
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• 2005

In the present paper, we observe a relation between fuzzy norms and induced crisp norms on a linear space. We first prove that if $\rho_1,\;\rho_2$ are equivalent fuzzy norms on a linear space, then for every $\varepsilon\in(0.1)$, the induced crisp norms $P_\varepsilon^1,\;and\;P_\varepsilon^2$, respectively are equivalent. Since the converse does not hold, we prove it under some strict conditions. And consider the following theorem proved in [8]: Let $\rho$ be a lower semicontinuous fuzzy norm on a normed linear space X, and have the bounded support. Then $\rho$ is equivalent to the fuzzy norm $\chi_B$ where B is the closed unit ball of X. The lower semi-continuity of $\rho$ is an essential condition which guarantees the continuity of $P_\varepsilon$, where 0 < e < 1. As the last result, we prove that : if $\rho$ is a fuzzy norm on a finite dimensional vector space, then $\rho$ is equivalent to $\chi_B$ if and only if the support of $\rho$ is bounded.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.269-271
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• 2002

In this paper, we consider the fuzzy linear regression line for fuzzy number data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.306-311
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• 2008

Fuzzy regression, a nonparametric method, can be quite useful in estimating the relationships among variables where the available data are very limited and imprecise. It can also serve as a sound methodology that can be applied to a variety of management and engineering problems where variables are interacting in an uncertain, qualitative, and fuzzy way. A close examination of the fuzzy regression algorithm reveals that the resulting possibility distribution of fuzzy parameters, which makes this technique attractive in a fuzzy environment, is dependent upon an h parameter value. The h value, which is between 0 and 1, is referred to as the degree of fit of the estimated fuzzy linear model to the given data, and is subjectively selected by a decision maker (DM) as an input to the model. The selection of a proper value of h is important in fuzzy regression, because it determines the range of the posibility ditributions of the fuzzy parameters. In this paper, we discuss the interdependent relationship among the h value, membership function shape, and the spreads of fuzzy parameters in fuzzy linear regression with fuzzy input-output using shape-preserving operations.