• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 춘계학술대회 논문집
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• pp.75-80
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• 1999

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcome limitation of accuracy in the linear regression model or the engineering judgment model. And this model is compared with the engineering judgment model. It is not necessary complex process such like multi-regression analysis of the engineering judgment model. A fuzzy model does not need to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Like a regression model, this model can be applied to any machine, but it delivers greater accuracy and robustness.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.445-449
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• 2005

Yen et al. [Fuzzy Sets and Systems 106 (1999) 167-177] calculated the fuzzy membership function for the output to find the non-symmetric triangular fuzzy number coefficients of a linear regression model for all given input-output data sets. In this note, we show that the result they obtained in their paper is invalid.

• 한국지능시스템학회논문지
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• 제23권4호
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• pp.366-370
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• 2013

설명변수와 반응변수 사이의 통계적 관계를 설명하기 위해 사용되는 회귀모형을 분석하는 방법을 회귀분석이라 한다. 본 논문에서는 독립변수와 종속변수에 대한 퍼지관계를 표현하는 퍼지회귀모형를 추정하기 위하여 이상치에 민감하지 않은 로버스트한 추정량인 Theil방법을 소개한다. Theil방법은 설명변수와 반응변수의 ${\alpha}$-수준집합의 각 성분으로 구성된 집합에서 선택한 임의의 두 쌍 자료로부터 계산된 변화율의 중위수를 두 변수에 대한 변화량의 추정량으로 간주한다. 본 논문에서 제안된 Theil방법이 최소자승법을 이용하여 추정된 퍼지회귀모형보다 더 정확할 수 있음을 예제를 통하여 확인한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권4호
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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.

• 한국지능시스템학회논문지
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• 제8권6호
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• pp.99-105
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• 1998

이 논문은 퍼지비선형회귀모형에 대한 것으로서 유전적 알고리즘을 이용한 퍼지회귀분석모형을 제안한다. 유전적 알고리즘이란 좀 더 나은 퍼지회귀분석을 위하여 입력데이터를 분류하는데 사용되어진다. 이 분할에서 각 데이터는 분류된 데이터그룹에 속하는 멤버쉽함수의 값을 가지게 된다. 데이터그룹은 각 변수의 영역을 최적으로 분할함에 따라 몇 개의 퍼지선형회귀모형에서 서로 다른 퍼지파라메타를 가지게 된다. 데이터에 대한 최종 퍼지수를 얻기 위하여 각 데이터그룹의 퍼지출력을 구성한다. 이 방법의 유효성은 사례연구에 의하여 보이고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.306-310
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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.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.799-813
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• 2008

This paper deals with the properties of the fuzzy least squares estimators for fuzzy linear regression model. Especially fuzzy triangular input-output model including error term is proposed. The error term is considered as a fuzzy random variable. The asymptotic unbiasedness and the consistency of the estimators are proved using a suitable metric.

• 한국지능시스템학회논문지
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• 제18권6호
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• pp.832-836
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• 2008

퍼지회귀분석은 독립변수들과 종속변수간의 관계를 평가하는데 사용된다. 만약 언어적 표현으로 된 자료를 처리할 때 일반적인 회귀분석을 사용한다면 과도한 단순화 때문에 어느 정도 한계를 가진다. 본 논문에서는 프로젝트의 성과를 예측하기 위해 퍼지 입출력을 갖는 퍼지회귀분석을 사용한다.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.643-654
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• 2000

In this paper, we review some class of t-norms on which fuzzy arithmetic operations preserve the shapes of fuzzy numbers and the Hausdorff-distance between fuzzy numbers as the measure of distance between fuzzy numbers. And we suggest the least Hausdorff-distance square method for fuzzy linear regression model using shape preserving fuzzy arithmetic operations.