کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
8057657 1520057 2018 13 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Development of an optimized trend kriging model using regression analysis and selection process for optimal subset of basis functions
ترجمه فارسی عنوان
توسعه یک مدل کریگینگ روند بهینه شده با استفاده از تحلیل رگرسیون و فرایند انتخاب برای زیر مجموعه های بهینه توابع پایه
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی هوافضا
چکیده انگلیسی
Surrogate modeling, or metamodeling, is an efficient way of alleviating the high computational cost and complexity for iterative function evaluation in design optimization. Accuracy is significantly important because optimization algorithms rely heavily on the function response calculated by surrogate model and the optimum solution is directly affected by the quality of surrogate model. In this study, an optimized trend kriging model is proposed to improve the accuracy of the existing kriging models. Within the framework of the proposed model, regression analysis is carried out to approximate the unknown trend of the true function and to determine the order of the universal kriging model, which has a fixed form with a mean structure dependent on the order of model. In addition, the selection of an optimal basis function is conducted to separate the useful basis function terms from the full set of the basis function. The optimal subset of the basis function is selected with the global optimization algorithm; which can accurately represent the trend of true response surface. The mean structure of proposed model has been optimized to maximize the accuracy of kriging model depending on the trend of true function. Two- and three-dimensional analytic functions and a practical engineering problem are chosen to validate the proposed model. The results showed that the OTKG model yield the most accurate responses regardless of the number of initial sample points, and can conversed into well-trained model with few additional sample points.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Aerospace Science and Technology - Volume 77, June 2018, Pages 273-285
نویسندگان
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