• Title/Summary/Keyword: fuzzy spline

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APPROXIMATION BY FUZZY B-SPLINE SERIES

  • BLAGA PETRU;BEDE BARNABAS
    • Journal of applied mathematics & informatics
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    • v.20 no.1_2
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    • pp.157-169
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    • 2006
  • We study properties concerning approximation of fuzzy-number-valued functions by fuzzy B-spline series. Error bounds in approximation by fuzzy B-spine series are obtained in terms of the modulus of continuity. Particularly simple error bounds are obtained for fuzzy splines of Schoenberg type. We compare fuzzy B-spline series with existing fuzzy concepts of splines.

Fuzzy System Representation of the Spline Interpolation for differentiable functions

  • Moon, Byung-Soo
    • 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.

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A continuous solution of the heat equation based on a fuzzy system

  • Moon, Byung-Soo;Hwang, In-Koo;Kwon, Kee-Choon
    • 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

A Study on Fuzzy Wavelet Basis Function for Image Interpolation

  • Byun, Oh-Sung;Moon, Sung-Ryong
    • 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.

Multi-Sensor Data Fusion Model that Uses a B-Spline Fuzzy Inference System

  • Lee, K.S.;S.W. Shin;D.S. Ahn
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.23.3-23
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    • 2001
  • The main object of this work is the development of an intelligent multi-sensor integration and fusion model that uses fuzzy inference system. Sensor data from different types of sensors are integrated and fused together based on the confidence which is not typically used in traditional data fusion methods. The information is fed as input to a fuzzy inference system(FIS). The output of the FIS is weights that are assigned to the different sensor data reflecting the confidence En the sensor´s behavior and performance. We interpret a type of fuzzy inference system as an interpolator of B-spline hypersurfaces. B-spline basis functions of different orders are regarded as a class of membership functions. This paper presents a model that ...

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Preliminary Hull Form Generation Using Fuzzy Model (Fuzzy 모델을 이용한 초기선형 생성)

  • Soo-Young Kim;Yeon-Seung Lee
    • Journal of the Society of Naval Architects of Korea
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    • v.29 no.4
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    • pp.36-44
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    • 1992
  • To improve the B-spline form-parameter method being used in preliminary hull form generation, this research considers fuzzy modeling of the relationships among form-parameters based on the actual ship data analysis. Form-parameter values are determined through fuzzy inference. To verify the validity of the proposed fuzzy model the hull forms of actual ships are compared with hull forms generated by fuzzy model.

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Generation of Sectional Area Curve using an ANFIS and a B-spline Curve (적응형 회로망의 퍼지 추론과 B-spline 곡선을 이용한 횡단면적 곡선의 생성)

  • Kim, Soo-Young;Kim, Hyun-Cheol;Ryeu, Kyung-Hyun;Kim, Min-Jeong
    • Journal of Ocean Engineering and Technology
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    • v.12 no.3 s.29
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    • pp.96-102
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    • 1998
  • This paper presents to create a SAC(Sectional Area Curve) using an ANFIS(Adaptive-Network-based Fuzzy Inference System). First, it defines SACs of parent ships by using a B-spline approximation and a genetic algorithm and accumulates a database about SAC's control points. Second, it learns an ANFIS from parent ship data, which are related with principal dimensions and SAC's control points. This process is to model an ANFIS for SAC inferreice. When an ANFIS modeling is completed, we can determine a SAC through an ANFIS inferring.

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INTERPOLATION OF FUZZY DATA BY NATURAL SPLINES

  • Abbasbandy, S.;Babolian, E.
    • 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.

Solution of the boundary value problem for the second order ordinary differential equations by a fuzzy system (2계 선형상미방 경계치문제의 퍼지시스템 해법)

  • 문병수;정종은;황인구;김정수
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2002.05a
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    • pp.289-292
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    • 2002
  • 2계 선형 상미분방정식의 경계치 문제는 보통 해를 구하고자 하는 구간의 양 끝점에서 도함수의 값을 임의로 선정한 후 각 점에서 초기치 문제의 해를 구한 다음 적절한 1차 결합을 이용하여 구하게 된다. 이 경우 초기값과 도함수 값을 사용한 반복연산이 수반되며 따라서 오차의 누적이 불가피 하게 된다. 이 논문에서는 이같은 오차의 누적을 피할 뿐 아니라 3차 Spline 함수를 사용함으로써 오차가 O( $h^2$)인 해를 구하는 방법에 대하여 기술한다 두 개의 경계조건과 근사값을 구하고자 하는 점에서의 함수 값을 "If x is $B_{i}$, then f is $C_{i}$"와 같은 Fuzzy Rule들로 변형하고 주어진 미분방정식을 상수 $C_{i}$들의 관계식으로 변형하여 해를 구하였다. 산출된 결과로부터의 보간 연산은 Fuzzy System사용에 의하여 대체되었다. 이상의 방법으로 산출한 해의 근사오차가 O( $h^2$).임을 증명하였으며 3개의 예제에 대한 계산결과를 4계 Runge-Kutta 방법에 의한 해와 비교하여 기술하였다였다였다였다

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