دانلود مقاله ISI انگلیسی شماره 24204
ترجمه فارسی عنوان مقاله

برازش و انتخاب متغیر در رگرسیون خطی چند فازی

عنوان انگلیسی
Goodness of fit and variable selection in the fuzzy multiple linear regression
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
24204 2006 21 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Fuzzy Sets and Systems, Volume 157, Issue 19, 1 October 2006, Pages 2627–2647

ترجمه کلمات کلیدی
تجزیه و تحلیل رگرسیون خطی فازی - متغیرهای ورودی واضح - متغیر خروجی فازی متقارن - حداقل مربعات نزدیک - روش اکتشافی - اتصالات اندازه گیری - تجزیه انحراف - ضریب تعیین چندگانه - ضریب تعدیل تعیین متعدد - اندازه گیری - معیارهای انتخاب متغیر
کلمات کلیدی انگلیسی
Fuzzy linear regression analysis, Crisp input variables, Symmetrical fuzzy output variable, Least-squares approach, Fitting measure, Deviance decomposition, Coefficient of multiple determination, Adjusted coefficient of multiple determination, Mallows measure, Variable selection criteria,
پیش نمایش مقاله
پیش نمایش مقاله  برازش و انتخاب متغیر در رگرسیون خطی چند فازی

چکیده انگلیسی

In performing a fuzzy multiple linear regression model, important topics are: to measure the fitting quality of the model and to find the “best” set of input variables that explain the variation in the observed system responses. In this paper, by considering an exploratory approach, to express the quality of fit of a fuzzy linear regression model, a coefficient of multiple determination R2R2 for symmetrical fuzzy variable has been suggested. Furthermore, for overcoming the inconveniences of R2R2 an adjusted version of R2R2 (denoted by View the MathML sourceR¯2) has been defined. For measuring the fitting performances of the estimated model, a fuzzy extension of another goodness of fit measure, the so-called Mallows index (Cp)(Cp), has been considered. All the proposed fitting measures have been utilized for selecting suitably the input variables of a fuzzy linear regression model. To this purpose, some variable selection procedures based on R2R2, View the MathML sourceR¯2 and CpCp have been suitably extended in a fuzzy framework. To explain the efficacy of the goodness of fit measures and the variable selection criteria some examples are also shown.