پديد آورنده :
فتاحي، سعيد
عنوان :
ارزيابي مهندسي نخ پنبه اي با استفاده از مدلهاي رگرسيون آماري و فازي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده مهندسي نساجي
صفحه شمار :
ده،117ص.: مصور،جدول،نمودار
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
عبدالكريم حسيني راوندي، محمود طاهري
توصيفگر ها :
آزمون چند متغيره , آناليز واريانس چند متغيره , رگرسيون چند متغيره چند گانه , رگرسيون حداقل مربعات فازي , معيار نيكوئي برازش , رگرسيون استوار , ريسندگي پنبه , عيوب نخ , ماشين ريسندگي رينگ
تاريخ نمايه سازي :
27/9/89
استاد داور :
محمد عترتي، سروش عليمرادي، محمد قانع
كد ايرانداك :
ID333 دكتري
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
The Evaluation of Cotton Yarn Engineering Using Statistical and Fuzzy Regression Models Saeid Fattahi Email SFattahi@yazduni ac ir Date of Submission 20 October 2010 Department of Textile Engineering Isfahan University of Technology Isfahan 84156 83111 Iran S A Hoseini Ravandi1 Email hoseinir@cc iut ac ir S M Taheri2 Email Taheri@cc iut ac ir 1 Department of Textile Engineering Isfahan University of Technology Isfahan 2 Department of Mathematical Sciences Isfahan University of Technology Isfahan AbstractThis research explains the feasibility of two way prediction by developing direct models relating fiber to yarn and diverse models relating yarn to fiber using multivariate methods simultaneously On the other hand we investigate a procedure to provide a soft method for modeling the relationships between fiber properties roving properties and yarn count as independent variables and yarn properties as dependent response variable To this end cotton fiber properties were measured from rovings carefully untwisted HVI systemand evenness tester of premier were used to measure the various properties The samples of cotton yarns 108samples produced yarn counts ranging from 16 to 32 Ne with optimum twist factor In this study effectivevariables were selected by multivariate statistical test mtest Multivariate analysis of variance MANOVA was first used for evaluating the significant of obtained models Then considered fuzzy least squaresregression for evaluating relationship between cotton yarn properties such as tensile hairiness unevennessand fiber properties that were measured by HVI system We also used mean of capability index MCI toevaluate the goodness of fit of the fuzzy regression models The results showed that the equations weresignificant at good levels Keywords Multivariate test mtest Multivariate analysis of variance MANOVA Multivariate multipleregression Cotton spinning Quality properties of yarn Cotton fiber properties Fuzzy least squaresregression Mean of capability index Introduction Predicting the qualitative characteristics of yarns such as tensile properties unevenness and hairiness from the raw material properties has been the main purpose ofmany studies in recent decades Two main approaches used in these studies are statisticaland mathematical approach Statistical models have relatively higher predictive power thanmathematical models One of the most common statistical approaches to predict yarnproperties is the multiple regression method So far statistical models for the prediction ofcotton yarn properties from fiber properties have been established 1 2 Prediction ofcotton yarn properties from fiber properties have also been reviewed in details 3 In statistical regression we can make estimates and predictions for the dependentvariables based on a set of observed data But in systems in which human intelligenceparticipates we usually encounter the following two cases 1 the relation between variables are imprecise and2 the variables themselves are fuzzy
استاد راهنما :
عبدالكريم حسيني راوندي، محمود طاهري
استاد داور :
محمد عترتي، سروش عليمرادي، محمد قانع
