دانلود مقاله ISI انگلیسی شماره 79728
ترجمه فارسی عنوان مقاله

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

عنوان انگلیسی
Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
79728 2010 13 صفحه PDF
منبع

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

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

ترجمه کلمات کلیدی
برنامه نویسی ژنتیک؛ رگرسیون فازی؛ تشخیص نقاط دورافتاده - توزیع روند اپوکسی
کلمات کلیدی انگلیسی
Genetic programming; Fuzzy regression; Outlier detection; Epoxy dispensing process
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.