• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.223-226
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• 2002

This Paper presents an interval type-2 fuzzy perceptron algorithm that is an extension of the type-1 fuzzy perceptron algorithm proposed in [1]. In our proposed method, the membership values for each Pattern vector are extended as interval type-2 fuzzy memberships by assigning uncertainty to the type-1 memberships. By doing so, the decision boundary obtained by interval type-2 fuzzy memberships can converge to a more desirable location than the boundary obtained by crisp and type-1 fuzzy perceptron methods. Experimental results are given to show the effectiveness of our method

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1390-1397
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• 1996

In this paper, in case of segmenting an image by a fuzzy entropy, an image segmentation algorithm is derived under an extended fuzzy entropy including the probabilistic including the probabilistic information in order to cover the toal uncertainty of information contained in fuzzy sets. By describing the image with fuzzysets, the total uncertainty of a fuzzy set consists of the uncertain information arising from its fuzziness and the uncertain information arising from the randomness in its ordinary set. To optimally segment all the boundary regions in the image, the total entropy function is computed by locally applving the fuzzy and Shannon entropies within the width of the fuzzy regions and the image is segmented withthe global maximum andlocal maximawhich correspond to the boundary regions. Comtional one by detecting theboundary regions more than 5 times.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.45-52
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• 2004

The sliding mode controller with a boundary layer implemented by simplified indirect inference method (SIIM) fuzzy logic was proposed. The components of the sliding line function are used for the inputs of the SIIM fuzzy logic. The proposed control system is simple because there is no need to derive the sigmoid function and there are only four rules. The overall stability of the proposed system and the boundness of the tracking error are proved easily using the Lyapunov theory. We apply the proposed controller to control a nonlinear time-varying system. The computer simulation showed the validity of the proposed control system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1810-1815
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• 2003

Sliding mode control with nonlinear interpolation in the boundary layer is proposed. A modified sigmoid function is used for nonlinear interpolation in the boundary layer and its parameter is tuned by a fuzzy logic controller. The fuzzy logic controller that takes the distance between the system state and the sliding surface as its input guides the choice of parameter of the modified sigmoid function and the parameter is on-line tuned. Owing to the decreased thickness, the proposed method has better tracking performance than the conventional linear interpolation method. To demonstrate its performance, the proposed control algorithm is applied to a simple nonlinear system model.

• The Pure and Applied Mathematics
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• v.27 no.4
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• pp.207-229
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• 2020

We establish fixed point and multidimensional fixed point results satisfying generalized (𝜓, 𝜃, 𝜑)-contraction on partially ordered non-Archimedean fuzzy metric spaces. By using this result we obtain the solution for periodic boundary value problems and give an example to show the degree of validity of our hypothesis. Our results generalize, extend and modify several well-known results in the literature.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.128-130
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• 1995

This paper presents the optimal operation scheduling on cogeneration systems connected with auxiliary equipment by using the possibility fuzzy theory. The probability fuzzy theory is a method to obtain the possibility of the solution from the fuzzification of coefficients. Simulation is carried out to obtain the boundary of heat production in each time interval. Simulation results shows effectively the flexible operation boundary to establish operation scheduling.

• Korean Journal of Mathematics
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• v.30 no.2
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• pp.375-389
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• 2022

In this paper, we prove some fixed-point theorems in partially ordered fuzzy metric spaces for (𝜙, 𝜓, 𝛽)-Geraghty contraction type mappings which are generalization of mappings with Geraghty contraction type condition. Application of the derived results are shown in proving the existence of unique solution to some boundary value problems.

• The Pure and Applied Mathematics
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• v.11 no.3
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• pp.243-265
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• 2004

In this paper, we introduce the fundamental concepts of intuitionistic fuzzy Q-neighborhood, intuitionistic Q-first axiom of countability, intuitionistic first axiom of countability, intuitionistic fuzzy closure operator, intuitionistic fuzzy boundary point and intuitionistic fuzzy accumulation point and investigate some of their properties.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.13-17
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• 2003

A continuous solution of the Dirichlet boundary value problem for the heat equation $u_t$＝$a2u_{xx}$ using a fuzzy system is described. We first apply the Crank-Nicolson method to obtain a discrete solution at the grid points for the heat equation. Then we find a continuous function to represent approximately the discrete values at the grid points in the form of a bicubic spline function (equation omitted) that can in turn be represented exactly by a fuzzy system. We show that the computed values at non-grid points using the bicubic spline function is much smaller than the ones obtained by linear interpolations of the values at the grid points. We also show that the fuzzy rule table in the fuzzy system representation of the bicubic spline function can be viewed as a gray scale image. Hence, the fuzzy rules provide a visual representation of the functions of two variables where the contours of different levels for the function are shown in different gray scale levels