کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
496864 | 862872 | 2009 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Study of multi-branch structure of Universal Learning Networks
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
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چکیده انگلیسی
In this paper, multi-branch structure of Universal Learning Networks (ULNs) is studied to verify its effectiveness for obtaining compact models, which have neurons connected with other neurons using more than two branches having nonlinear functions. Multi-branch structure has been proved to have higher representation/generalization ability and lower computational cost than conventional neural networks because of the nonlinear function of the multi-branches and the reduction of the number of neurons to be used. In addition, learning of delay elements of multi-branch ULNs has improved their potential to build up a compact dynamical model with higher performances and lower computational cost when applied for identifying dynamical systems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 9, Issue 1, January 2009, Pages 393–403
Journal: Applied Soft Computing - Volume 9, Issue 1, January 2009, Pages 393–403
نویسندگان
Shingo Mabu, Kaoru Shimada, Kotaro Hirasawa, Jinglu Hu,