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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/91

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contributor.authorDash, P K-
contributor.authorSatpathy, H P-
contributor.authorRahman, S-
date.accessioned2005-07-02T05:39:19Z-
date.available2005-07-02T05:39:19Z-
date.issued1995-
identifier.citationInternational Conference on Energy Management and Power Delivery(EMPD '95), 21-23 Nov. 1995, P 565 - 570 vol.2en
identifier.urihttp://hdl.handle.net/2080/91-
descriptionPersonal 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
description.abstractA fuzzy neural network based on the multilayer perceptron and capable of fuzzy classification of patterns is presented in this paper. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used for training the network. In the supervised learning phase linear Kalman filter equations are used for tuning the weights and membership functions. Extensive tests have been performed on a two-year-utility data for generation of peak and average load profiles for 24- and 168-hours ahead time frames and results for winter and summer months are given to confirm the effectiveness of the new approachen
format.extent608394 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectKalman filtersen
subjectfuzzy neural netsen
subjectload forecastingen
subjectmultilayer perceptronsen
titleShort term daily average and peak load predications using a hybrid intelligent approachen
typeArticleen
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