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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/196
Title: A reduced complexity adaptive legendre neural network for nonlinear active noise control
Authors: George N.V.
Panda G.
Keywords: Filtered-l LMS algorithm
Legendre neural network
Nonlinear active noise control
Partial updates
Issue Date: 2012
Citation: 14
Abstract: This paper proposes a novel low complexity nonlinear active noise control (ANC) system. The nonlinear controller is composed of an adaptive Legendre neural network (LeNN), updated using a filtered-l least mean square (FlLMS) algorithm. The computational complexity of the proposed scheme has been further reduced by incorporating the principle of partial update adaptive algorithms. Simulation study demonstrates comparable performance of the new ANC method with that of the conventional nonlinear ANC schemes, with reduced computational complexity. � 2012 Institute of Telecommunica.
URI: http://10.10.32.48:8080/jspui/handle/2008/196
Appears in Collections:Research Publications

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