Fuzzy Linear Regression Model
A new hybrid of fuzzy c means method and fuzzy linear regression model in predicting manufacturing income
... MLR model and fuzzy linear regression model that proposed by Tanaka, Ni and ...each model error value were discussing in the previous chapter on data explo- ration section to see ...
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Statistical Analysis of Fuzzy Linear Regression Model Based on Centroid Method
... Traditional regression analysis methods often require that both independent variable and dependent variable are clear ...traditional regression can not get satisfactory and completely ...
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Developing a univariate approach to phase-I monitoring of fuzzy quality profiles Pages 829-842 Download PDF
... a fuzzy linear regression model can represent the profile adequately where the response quality characteristic is ...in fuzzy environment. A model in fuzzy linear ...
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A Variable Structural Control for a Flexible Plate
... Linear regression models are used to model the functional relationship between the response and the predictors ...the linear assumption, the exactness of the relationship, and the existence of ...
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Fuzzy Regression Model and Its Application: A Review
... proposed fuzzy linear regression model with crisp input and fuzzy output described by Tanaka was considered ...modified fuzzy least square method for the ...conventional ...
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Error Estimation Using Fuzzy Linear Regression Analysis
... statistical regression analysis and fuzzy regression analysis, aims to determine the best-fit model for describing the functional relationship between dependent variables and independent ...
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A technique of fuzzy c mean in multiple linear regression model toward paddy yield
... Especially, fuzzy clustering has been widely studied and applied in a variety of key areas and fuzzy cluster validation plays a very important role in fuzzy ...MLR model with other method ...
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Comparison between multiple linear regression and fuzzy C regression models towards scale of health in ICU
... multiple linear regression (MLR) model is well-known in analyzing linear ...the fuzzy c-regression models (FCRM) are being widely used in analyzing the nonlinear ...better ...
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Aircraft taxi time prediction: comparisons and insights
... erent regression approaches for predicting taxi times at airports to demonstrate the performance of ...multiple linear regression, least median squared linear regression, support vector ...
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A WEIGHTED GOAL PROGRAMMING APPROACH TO FUZZY LINEAR REGRESSION WITH QUASI TYPE-2 FUZZY INPUT-OUTPUT DATA
... The existence of outliers in a set of experimental data can cause incorrect interpretation of the fuzzy linear regression results. To handle the outlier problem, Hung and Yang [16] proposed an ...
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Fuzzy Regression Model Based on Fuzzy Distance Measure
... the fuzzy regression method, but some methods are not rigorous when it comes to error estimation and sometimes there are some special requirements for the object of study, such as the observations should be ...
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... Forecasting model based on BP Neural Network for Crude Oil Production and Consumption in China was carried out by ...Multi linear regression has been used for oilfield output prediction in some ...
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On the use of fuzzy c regression truncated models for health indicator in intensive care unit
... The models/technique discussed are multiple linear regression MLR model, analytical hierarchy process AHP technique, fuzzy c-means FCM model, fuzzy c-regression models FCRM, fuzzy linear[r] ...
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Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia’s hospital
... new fuzzy approach for the automatic segmentation of normal and pathological brain magnetic resonance imaging ...The model is fuzzy c-means (FCM) ...well-known fuzzy and non-fuzzy ...
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Plotting partial correlation and regression in ecological studies
... multiple regression model (Belsey et ...Partial regression plots have also been called added variable plots, adjusted variable plots and partial regression leverage plots (Belsey et ...a ...
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Linear regression in the parameters study of an econometric model
... [4] Karamé, F. Economètrie M1 Assurance et analyse financière. Maine : Université du Maine, 2012. [5] JACQUEMET, BRUNO CREPON et NICOLAS. Econometrie linéaire appliquée. PARIS : s.n., 2006. [6] Bourbonnais, R. ...
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Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities
... many regression-based methods have been proposed that use auxiliary spatial data sets to re- fine coarse-grained target data (Flaxman, Wang, and Smola 2015; Smith, Mashhadi, and Capra 2014; Wang et ...2015). ...
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A fuzzy linear fractional programming problem with fuzzy homogeneous constraints in trapezoidal fuzzy numbers
... transforms the given problem in to another FLFPP with fewer fuzzy constraints. A relationship between these two problems, which ensure that the solution of the original problem can be recovered from the solution ...
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On Inference of the Linear Regression Model with Groupwise Heteroscedasticity
... The most commonly used heteroscedasticity consistent covariance matrix estimator (HCCME) was presented by White (1980). White’s estimator is known as HC0 in the literature. MacKinnon and White (1985) and Davidson and ...
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Wear Optimization of Al/Sic/Gr Hybrid MMC Using Taguchi and ANOVA
... From the above eq’s (1- 3) it indicates that load, speed and track dia, increases or decreases the wear rate and coefficient of friction at any parametric value. the positive sign indicates that increase in load ...
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