دانلود مقاله ISI انگلیسی شماره 151516
ترجمه فارسی عنوان مقاله

بلوک برنامه های ساختمانی برای رگرسیون نمادین

عنوان انگلیسی
Block building programming for symbolic regression
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
151516 1980 8 صفحه PDF
منبع

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

Journal : Neurocomputing, Volume 275, 31 January 2018, Pages 1973-1980

ترجمه کلمات کلیدی
رگرسیون نمادین، تابع جداگانه، بلوک برنامه های ساختمانی، برنامه نویسی ژنتیک،
کلمات کلیدی انگلیسی
Symbolic regression; Separable function; Block building programming; Genetic programming;
پیش نمایش مقاله
پیش نمایش مقاله  بلوک برنامه های ساختمانی برای رگرسیون نمادین

چکیده انگلیسی

Symbolic regression that aims to detect underlying data-driven models has become increasingly important for industrial data analysis. For most existing algorithms such as genetic programming (GP), the convergence speed might be too slow for large-scale problems with a large number of variables. This situation may become even worse with increasing problem size. The aforementioned difficulty makes symbolic regression limited in practical applications. Fortunately, in many engineering problems, the independent variables in target models are separable or partially separable. This feature inspires us to develop a new approach, block building programming (BBP). BBP divides the original target function into several blocks, and further into factors. The factors are then modeled by an optimization engine (e.g. GP). Under such circumstances, BBP can make large reductions to the search space. The partition of separability is based on a special method, block and factor detection. Two different optimization engines are applied to test the performance of BBP on a set of symbolic regression problems. Numerical results show that BBP has a good capability of structure and coefficient optimization with high computational efficiency.