Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/397
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dash, P K | - |
dc.contributor.author | Samantaray, S R | - |
dc.date.accessioned | 2007-01-08T10:45:00Z | - |
dc.date.available | 2007-01-08T10:45:00Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | Engineering Intelligent Systems, Vol 4, P 205-210 | en |
dc.identifier.uri | http://hdl.handle.net/2080/397 | - |
dc.description | Copyright for this article belongs to the publisher | en |
dc.description.abstract | The paper presents a new fault classification scheme for high speed relaying using minimal radial basis function neural network. Unlike earlier approaches in using radial basis function network, the new approach reduces the training time drastically and provides a systematic framework for selecting the number of neurons in the hidden layer. Further the minimal radial basis function network yields an accurate fault type classification on a transmission line even in the presence of high fault resistance in the fault path. The paper also presents two different approaches in generating the inputs to the neural network with a view to simplify the training procedure and reduce the complexity in calculations. Several computer simulated test results are presented to highlight the effectiveness of the new approach. | en |
dc.format.extent | 358314 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | CRC Publications | en |
dc.subject | RBF neural network | en |
dc.subject | Fault Classification | en |
dc.subject | Network Input Generation | en |
dc.title | An accurate fault classification algorithm using a minimal radial basis function neural network | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.