دانلود مقاله ISI انگلیسی شماره 79728
عنوان فارسی مقاله

مدل سازی فرآیندهای تولید با استفاده از رگرسیون فازی مبتنی بر برنامه نویسی ژنتیک با تشخیص نقاط پرت

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79728 2010 13 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers
منبع

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

Journal : Information Sciences, Volume 180, Issue 4, 15 February 2010, Pages 506–518

کلمات کلیدی
برنامه نویسی ژنتیک؛ رگرسیون فازی؛ تشخیص نقاط دورافتاده - توزیع روند اپوکسی
پیش نمایش مقاله
پیش نمایش مقاله مدل سازی فرآیندهای تولید با استفاده از رگرسیون فازی مبتنی بر برنامه نویسی ژنتیک با تشخیص نقاط پرت

چکیده انگلیسی

Fuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka’s FR and Peters’ FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.