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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/397
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| Title: | An accurate fault classification algorithm using a minimal radial basis function neural network |
| Authors: | Dash, P K
Samantaray, S R |
| Keywords: | RBF neural network
Fault Classification
Network Input Generation |
| Issue Date: | 2004 |
| Publisher: | CRC Publications |
| Citation: | Engineering Intelligent Systems, Vol 4, P 205-210 |
| 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. |
| Description: | Copyright for this article belongs to the publisher |
| URI: | http://hdl.handle.net/2080/397 |
| Appears in Collections: | Journal Articles
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