کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8895312 1629899 2017 34 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Variable complexity online sequential extreme learning machine, with applications to streamflow prediction
ترجمه فارسی عنوان
پیچیدگی متغیر آنلاین متوالی دستگاه یادگیری افراطی، با برنامه های کاربردی برای پیش بینی جریان
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی
In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 555, December 2017, Pages 983-994
نویسندگان
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