کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1825940 | 1027371 | 2010 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Machine learning for the identification of scaling laws and dynamical systems directly from data in fusion
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
ابزار دقیق
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Machine learning for the identification of scaling laws and dynamical systems directly from data in fusion Machine learning for the identification of scaling laws and dynamical systems directly from data in fusion](/preview/png/1825940.png)
چکیده انگلیسی
Original methods to extract equations directly from experimental signals are presented. These techniques have been applied first to the determination of scaling laws for the threshold between the L and H mode of confinement in Tokamaks. The required equations can be extracted from the weights of neural networks and the separating hyperplane of Support Vector Machines. More powerful tools are required for the identification of differential equations directly from the time series of the signals. To this end, recurrent neural networks have proved to be very effective to properly identify ordinary differential equations and have been applied to the coupling between sawteeth and ELMs.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment - Volume 623, Issue 2, 11 November 2010, Pages 850–854
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment - Volume 623, Issue 2, 11 November 2010, Pages 850–854
نویسندگان
A. Murari, J. Vega, D. Mazon, D. Patané, G. Vagliasindi, P. Arena, N. Martin, N.F. Martin, G. Rattá, V. Caloone,