کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6922809 | 865082 | 2014 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river
ترجمه فارسی عنوان
مقایسه تعداد زیادی از روشهای فراشناختی برای آموزش شبکه عصبی مصنوعی برای پیش بینی دمای آب در یک رودخانه طبیعی
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کلمات کلیدی
متهوریستی، پیش بینی دمای جریان جریان، تغییر آب و هوا، شبکه های عصبی مصنوعی، تکامل دیفرانسیل، بهینه سازی ذرات ذرات،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
In this paper the detailed comparison of the performance of nature-inspired optimization methods and Levenberg-Marquardt (LM) algorithm in ANNs training is performed, based on the case study of water temperature forecasting in a natural stream, namely Biala Tarnowska river in southern Poland. Over 50 variants of 22 various metaheuristics, including a large number of Differential Evolution, as well as some Particle Swarm Optimization, Evolution Strategies, multialgorithms and Direct Search methods are compared with LM algorithm on ANN training for the described case study. The impact of population size and some control parameters of particular metaheuristics on the ANN training performance are verified. It is found that despite widely claimed large improvement in nature-inspired methods during last years, the vast majority of them are still outperformed by LM algorithm on the selected problem. The only methods that, based on this case study, seem competitive to LM algorithm in terms of the final performance (but not speed) are Differential Evolution algorithms that benefit from the concept of Global and Local neighborhood-based mutation operators. The streamwater forecasting performance of the neural networks is adequate, the major prediction errors are related to the river freezing and melting processes that occur during winter in the mountainous catchment under study.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Geosciences - Volume 64, March 2014, Pages 136-151
Journal: Computers & Geosciences - Volume 64, March 2014, Pages 136-151
نویسندگان
Adam P. Piotrowski, Marzena Osuch, Maciej J. Napiorkowski, Pawel M. Rowinski, Jaroslaw J. Napiorkowski,