پديد آورنده :
طاهري انداني، فريبا
عنوان :
مدل هاي رگرسيون تصادفي-فازي براساس فاصله هاي اطمينان
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
آمار رياضي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي
صفحه شمار :
[هفت]،119ص.: مصور،جدول
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
محمود طاهري
توصيفگر ها :
اميد رياضي , اندازه اعتبار
تاريخ نمايه سازي :
20/4/91
استاد داور :
محمدرضا احمدزاده، صفيه محمودي
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Abstract The classical regression model leads to effective statistical analysis of precise numeric andstatistical data Over the past two decades in order to cope with imprecise data comingfrom fuzzy environments where human expert subjective estimates are used various fuzzyregression models have been introduced Most of the existing studies on fuzzy regressionanalysis have focused on data consisting of numeric values interval like numbers or fuzzynumbers without randomness into considerasion In practical situations however thereexists a genuine need to cope with data that involves the factors of both fuzziness andprobability In order to address regression problems in the presence of such hybrid uncertaindata fuzzy random variables are introduced in this thesis to serves as an integralcomponent of regression models A new class of fuzzy regression models that is based onfuzzy random data is built and is called the confidence interval based fuzzy randomregression model CI FRRM First a general fuzzy regression model for fuzzy random datais introduced Then based on credibility measure the expected value of a fuzzy variable ispresented by using expectation and variances of fuzzy random variables sigma confidenceinterval are constructed for fuzzy random input output data The CI FRRM is establishedbased on the sigma confidence intervals The proposed regression model gives rise to anonlinear programming problem that consists of fuzzy numbers or interval numbers Sincesign changes in the fuzzy coefficients modify the entire programming structure of the solutionprocess the inherent dynamic nonlinearity of this optimization makes it difficult to exploit thetechniques of linear programming or classical nonlinear programming To remove thisdifficulty and derive the optimal model we consider two approaches a vertic method todescribe the model and a realistic model Finally explanatory examples is provided toillustrate and investigate the proposed fuzzy random regression model
استاد راهنما :
محمود طاهري
استاد داور :
محمدرضا احمدزاده، صفيه محمودي
