• Title/Summary/Keyword: fuzzy regression model

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

  • Koh, Taek-Beom
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.334-340
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    • 2002
  • This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

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

  • Yoon, Jin-Hee;Choi, Seung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.16 no.5
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    • pp.781-790
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    • 2009
  • This paper deals with a rank transformation method and a Theil's method based on an ${\alpha}$-level set of a fuzzy number to construct a fuzzy linear regression model. The rank transformation method is a simple procedure where the data are merely replaced with their corresponding ranks, and the Theil's method uses the median of all estimates of the parameter calculated from selected pairs of observations. We also consider two numerical examples to evaluate effectiveness of the fuzzy regression model using the proposed method and of another fuzzy regression model using the least square method.

A Note on Fuzzy Linear Regression Analysis of Fuzzy Valued Variables

  • Hong, Dug-Hun
    • Journal of the Korean Data and Information Science Society
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    • v.12 no.1
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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

  • Hwang, Chang-Ha;Hong, Dug-Hun;Lee, Sang-Bock
    • 한국데이터정보과학회:학술대회논문집
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    • 2004.10a
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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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    • v.16 no.1
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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
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09a
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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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    • v.17 no.2
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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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    • v.5 no.2
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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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    • v.17 no.3
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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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Landslide susceptibility mapping using Logistic Regression and Fuzzy Set model at the Boeun Area, Korea (로지스틱 회귀분석과 퍼지 기법을 이용한 산사태 취약성 지도작성: 보은군을 대상으로)

  • Al-Mamun, Al-Mamun;JANG, Dong-Ho
    • Journal of The Geomorphological Association of Korea
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    • v.23 no.2
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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.