Comparison of BP and GRNN algorithm for factory monitoring

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Title:	Comparison of BP and GRNN algorithm for factory monitoring
Authors:	宋文財
Contributors:	電機工程(學)系
Date:	2011-04
Issue Date:	2017-11-14 14:17:56 (UTC+8)
Abstract:	Artificial neural networks (ANNs) are one of the most recently explored advanced technologies which show promise in the factory monitoring area. This paper focuses on two particular network models, back-propagation network (BPN) and general regression neural network (GRNN). The prediction accuracy of these two models is evaluated using a practical application situation in a monitor factory. GRNN emerged as a variant of the artificial neural network. Its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. According the simulation results we can show that GRNN is an effective way to considerably improve the predictive ability of BPN.
Relation:	Applied Mechanics and Materials
Appears in Collections:	[Department of Electrical Engineering] 【電機工程系所】期刊論文

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