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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/251

Title: Adaptive training of artificial neural network
Authors: KHAPARDE, SA
PARNERKAR, A
HIREMATH, NS
SHESHAPRASAD, BJ
Keywords: backpropagation
feedforward neural nets
optimisation
power system harmonics
Issue Date: 1992
Publisher: IEEE
Citation: Tencon'92: Proceedings of the IEEE Region 10 International Conference on Technology Enabling Tomorrow : Computers, Communications and Automation towards the 21st Century (V 1), Melbourne, Australia, 11-13 November 1992, 525-529
Abstract: Adaptive training of a neural network for nonstationary processes is reported within the framework of a multilayer perceptron model using the backpropagation (BP) algorithm. The error introduced by small changes in system parameters is reflected to adapt the changes in the converged weight matrix. The error is minimized using a constrained optimization method like the gradient projection method (GPM). The method is applied for harmonic prediction in voltage waveforms. The results for a sample system are discussed.
URI: 10.1109/TENCON.1992.272004
http://hdl.handle.net/10054/251
http://dspace.library.iitb.ac.in/xmlui/handle/10054/251
ISBN: 0-7803-0849-2
Appears in Collections:Proceedings papers

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