A novel class of neural networks with quadratic junctions

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 會議論文 > Item 987654321/70269

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题名:	A novel class of neural networks with quadratic junctions
作者:	DeClaris, Nicholas;Su, Mu-chun
贡献者:	淡江大學電機工程學系
日期:	1991-10
上传时间:	2011-10-23 21:07:04 (UTC+8)
出版者:	IEEE
摘要:	The authors discuss the architecture and training properties of a multilayer feedforward neural network class that uses quadratic junctions in a neural architecture that uses effectively the backpropagation learning algorithm given by P.J. Werbos (1989). Both the architecture of the quadratic junctions and the backpropagation were adopted so as to endow the networks with appealing training properties (under supervision) and acceptable generalizations. Complexity and learning aspects of this class are examined and compared with traditional networks that use linear junctions.
關聯:	Proceedings of the IEEE international conferences on systems, man, and cybernetics, v.3,p.p1557 - 1562
DOI:	10.1109/ICSMC.1991.169910
显示于类别:	[電機工程學系暨研究所] 會議論文

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