کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
510259 865752 2008 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Adaptive explicit decision functions for probabilistic design and optimization using support vector machines
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Adaptive explicit decision functions for probabilistic design and optimization using support vector machines
چکیده انگلیسی

This article presents a methodology to generate explicit decision functions using support vector machines (SVM). A decision function is defined as the boundary between two regions of a design space (e.g., an optimization constraint or a limit-state function in reliability). The SVM-based decision function, which is initially constructed based on a design of experiments, depends on the amount and quality of the training data used. For this reason, an adaptive sampling scheme that updates the decision function is proposed. An accurate approximated explicit decision functions is obtained with a reduced number of function evaluations. Three problems are presented to demonstrate the efficiency of the update scheme to explicitly reconstruct known analytical decision functions. The chosen functions are the boundaries of disjoint regions of the design space. A convergence criterion and error measure are proposed. The scheme is also applied to the definition of an explicit failure region boundary in the case of the buckling of a geometrically nonlinear arch.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Structures - Volume 86, Issues 19–20, October 2008, Pages 1904–1917
نویسندگان
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