• 제목/요약/키워드: fuzzy regression model

검색결과 152건 처리시간 0.022초

클러스터 생성을 이용한 자기구성 퍼지 모델링 (Self-Organizing Fuzzy Modeling Using Creation of Clusters)

  • 고택범
    • 한국지능시스템학회논문지
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    • 제12권4호
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    • pp.334-340
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    • 2002
  • 본 논문에서는 상대적으로 큰 퍼지 엔트로피를 갖는 입력-출력 데이터 집단에 다중 회귀 분석을 적용하여 다차원 평면 클러스터를 생성하고, 이 클러스터를 새로운 퍼지 모델의 규칙으로 추가한 후 모델 파라미터의 개략 동조와 정밀 동조를 반복 수행하는 자기구성 퍼지 모델링을 제안한다 Weighted recursive least squared 알고리즘과 fuzzy C-regression model 클러스터링에 의해 퍼지 모델의 파라미터를 개략적으로 동조한 후 gradient descent 알고리즘에 의해 파라미터를 정밀 동조하면서 감수분열 유전 알고리즘을 이용하여 최적의 학습률을 탐색한다. 그리고, 자기구성 퍼지 모델링 기법을 이용하여 Box-Jenkins의 가스로 데이터, 비선형 다변수 정적 함수의 데이터, 하수처리 활성오니 공정과 Mackey-Glass 시계열 데이터의 모델링을 수행하고, 기존의 방법에 의한 모델링 결과와 비교하여 그 성능을 입증한다.

분포무관추정량을 이용한 퍼지회귀모형 (Fuzzy Linear Regression Using Distribution Free Method)

  • 윤진희;최승회
    • Communications for Statistical Applications and Methods
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    • 제16권5호
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    • pp.781-790
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    • 2009
  • 본 논문에서는 퍼지수를 포함한 모수적 회귀모형을 추정하기 위하여 분포무관추정량으로 알려진 순위 변환방법과 Theil 방법을 소개한다. 순위 변환방법은 퍼지수의 ${\alpha}$-수준집합의 중심과 폭에 대한 순위를 이용하고 Theil 방법은 ${\alpha}$-수준집합의 중심과 폭에 대한 추정한 값들의 중위수를 이용한다. 예제를 이용하여 분포무관추정량으로 추정된 퍼지회귀모형의 효율성을 최소자승법과 여러 가지 방법으로 추정된 퍼지회귀모형과 비교한다.

A Note on Fuzzy Linear Regression Analysis of Fuzzy Valued Variables

  • 홍덕헌
    • Journal of the Korean Data and Information Science Society
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    • 제12권1호
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    • pp.99-101
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    • 2001
  • In this note, we show that a linear regression model, using entropy and degree of nearness of fuzzy numbers, suggested by Wang and Li[FSS 36, 125-136] seems to be unreasonable by an example.

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Estimating Fuzzy Regression with Crisp Input-Output Using Quadratic Loss Support Vector Machine

  • 황창하;홍덕헌;이상복
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2004년도 추계학술대회
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    • pp.53-59
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    • 2004
  • Support vector machine(SVM) approach to regression can be found in information science literature. SVM implements the regularization technique which has been introduced as a way of controlling the smoothness properties of regression function. In this paper, we propose a new estimation method based on quadratic loss SVM for a linear fuzzy regression model of Tanaka's, and furthermore propose a estimation method for nonlinear fuzzy regression. This approach is a very attractive approach to evaluate nonlinear fuzzy model with crisp input and output data.

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Fuzzy Regression Model Using Trapezoidal Fuzzy Numbers for Re-auction Data

  • Kim, Il Kyu;Lee, Woo-Joo;Yoon, Jin Hee;Choi, Seung Hoe
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제16권1호
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    • pp.72-80
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    • 2016
  • Re-auction happens when a bid winner defaults on the payment without making second in-line purchase declaration even after determining sales permission. This is a process of selling under the court's authority. Re-auctioning contract price of real estate is largely influenced by the real estate business, real estate value, and the number of bidders. This paper is designed to establish a statistical model that deals with the number of bidders participating especially in apartment re-auctioning. For these, diverse factors are taken into consideration, including ratio of minimum sales value from the point of selling to re-auctioning, number of bidders at the time of selling, investment value of the real estate, and so forth. As an attempt to consider ambiguous and vague factors, this paper presents a comparatively vague concept of real estate and bidders as trapezoid fuzzy number. Two different methods based on the least squares estimation are applied to fuzzy regression model in this paper. The first method is the estimating method applying substitution after obtaining the estimators of regression coefficients, and the other method is to estimate directly from the estimating procedure without substitution. These methods are provided in application for re-auction data, and appropriate performance measure is also provided to compare the accuracies.

Fuzzy c-Logistic Regression Model in the Presence of Noise Cluster

  • Alanzado, Arnold C.;Miyamoto, Sadaaki
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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    • pp.431-434
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    • 2003
  • In this paper we introduce a modified objective function for fuzzy c-means clustering with logistic regression model in the presence of noise cluster. The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. In real application there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data.

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Switching Regression Analysis via Fuzzy LS-SVM

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • 제17권2호
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    • pp.609-617
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    • 2006
  • A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

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A Random Fuzzy Linear Regression Model

  • Changhyuck Oh
    • Communications for Statistical Applications and Methods
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    • 제5권2호
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    • pp.287-295
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    • 1998
  • A random fuzzy linear regression model is introduced, which includes both randomness and fuzziness. Estimators for the parameters are suggested, which are derived mainly using properties of randomness.

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A Study on the Support Vector Machine Based Fuzzy Time Series Model

  • Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • 제17권3호
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    • pp.821-830
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    • 2006
  • This paper develops support vector based fuzzy linear and nonlinear regression models and applies it to forecasting the exchange rate. We use the result of Tanaka(1982, 1987) for crisp input and output. The model makes it possible to forecast the best and worst possible situation based on fewer than 50 observations. We show that the developed model is good through real data.

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로지스틱 회귀분석과 퍼지 기법을 이용한 산사태 취약성 지도작성: 보은군을 대상으로 (Landslide susceptibility mapping using Logistic Regression and Fuzzy Set model at the Boeun Area, Korea)

  • 알-마문;장동호
    • 한국지형학회지
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    • 제23권2호
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    • pp.109-125
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    • 2016
  • This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.