• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.499-506
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• 1998

In this paper we will consider the interpolation of fuzzy data by fuzzy-valued natural splines. Finally we will give the nu-merical solution of the illustrative examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.266-270
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• 2004

The image interpolation is one of an image preprocessing process to heighten a resolution. The conventional image interpolation used much to concept that it put in other pixel to select the nearest value in a pixel simply, and use much the temporal object interpolation techniques to do the image interpolation by detecting motion in a moving picture presently. In this paper, it is proposed the image interpolation techniques using the fuzzy wavelet base function. This is applied to embody a correct edge image and a natural image when expand part of the still image by applying the fuzzy wavelet base function coefficient to the conventional B-spline function. And the proposal algorithm in this paper is confirmed to improve about 1.2831 than the image applying the conventional B-spline function through the computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.358-363
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• 1998

An algorithm for representing the cubic spline interpolation of differentiable functions by a fuzzy system is presented in this paper. The cubic B-spline functions which form a basis for the interpolation function are used as the fuzzy sets for input fuzzification. The ordinal number of the coefficient cKL in the list of the coefficient cij's as sorted in increasing order, is taken to be the output fuzzy set number in the (k, l) th entry of the fuzzy rule table. Spike functions are used for the output fuzzy sets, with cij's as support boundaries after they are sorted. An algorithm to compute the support boundaries explicitly without solving the matrix equation involved is included, along with a few properties of the fuzzy rule matrix for the designed fuzzy system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1151-1154
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• 1993

The purpose of the paper is to explain some heuristic, common sense suppositions of fuzzy control. It is shown that Fuzzy Control is a kind of quasilinear interpolation of prototypes. Control function can be sufficiently exact represented as piecewise-linear function. The best interpolation is connected with normalized intersected fuzzy sets.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.811.2-818
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• 2001

• Ocean Systems Engineering
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• v.9 no.2
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• pp.179-190
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• 2019

This paper deals with the problem of the global stabilization for a class of ocean structure systems. It is well known that, in general, the global asymptotic stability of the ocean structure subsystems does not imply the global asymptotic stability of the composite closed-loop system. The classical fuzzy inference methods cannot work to their full potential in such circumstances because given knowledge does not cover the entire problem domain. However, requirements of fuzzy systems may change over time and therefore, the use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. Designing a dynamic rule base yet needs additional information. In this paper, we demonstrate this proposed methodology is a flexible and general approach, with no theoretical restriction over the employment of any particular interpolation in performing interpolation nor in the computational mechanisms to implement fitness evaluation and rule promotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.34-38
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• 1996

This paper examines the relation between multidimensional linear interpolation(MDI) and fuzzy reasoning, and shows that an MDI is a special form of Tsukamoto's fuzzy reasoning. From this result, we found new possibility of defuzzification strategy.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.562-567
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• 1998

This paper examined the relations among the multidimensional linear interpolation(MDI) and fuzzy reasoning , and neural networks, and showed that an showed that an MDI is a special form of Tsukamoto's fuzzy reasoning and regularization networks in the perspective of fuzzy reasoning and neural networks, respectively. For this purposes, we proposed a special Tsukamoto's membership (STM) systemand triangular basis function (TBF) networks, Also we verified the condition when our proposed TBF becomes a well-known radial basis function (RBF).

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.171-177
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• 2008

In this paper we compared the performance of interpolation algorithms for Bayer patterned image sensors under the various illumination intensities. As the interpolation algorithms, we used bilinear color interpolation and adaptive fuzzy color interpolation and our experimentation shows that performance of interpolation algorithms depend on the lighting conditions; in low intensity of illumination, bilinear color interpolation with median filter performs best, in high intensity of illumination, adaptive fuzzy color interpolation performs best, and in between bilinear color interpolation performs best. This study suggested an interpolation scheme which applies different interpolation algorithm according to the intensity of the input image, resuting in the better image quality.