Application of neural nets for modelling partial discharge phenomenon

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/114

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DC Field	Value	Language
dc.contributor.author	Ghosh, S	-
dc.contributor.author	Kishore, N K	-
dc.date.accessioned	2005-07-21T07:13:44Z	-
dc.date.available	2005-07-21T07:13:44Z	-
dc.date.issued	2000	-
dc.identifier.citation	Proceedings of IEEE International Conference on Industrial Technology, 19-22 Jan. 2000, P 215 - 220 vol.1	en
dc.identifier.uri	http://hdl.handle.net/2080/114	-
dc.description	Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE	en
dc.description.abstract	The classical approach to modelling is a quasiempirical relationship based on experiments on single artificial voids of well defined geometry. Such methods restrict the validity to the range of inputs considered. Keeping all this in view, this work attempts to apply an artificial neural network (ANN) for the modelling in order to exploit flexibility of ANN modelling with a short time for development and reasonably high accuracy. The results indicate good agreement of the estimates with the published values with a MAE (mean absolute error) of as low as 1%	en
dc.format.extent	538746 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.publisher	IEEE	en
dc.subject	feedforward neural nets	en
dc.subject	insulation testing	en
dc.subject	multilayer perceptrons	en
dc.subject	partial discharges	en
dc.title	Application of neural nets for modelling partial discharge phenomenon	en
dc.type	Article	en
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