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Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization

Abrar, Shafayat and Zerguine, A. and Bettayeb, M. (2002) Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization. Neural Networks, IEEE Transactions on, 13.

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Abstract

Stop-and-go decision-directed (S&G-DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol-interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust results with a lower mean-square error at the expense of slow convergence. To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. Simulation results show the effectiveness of the proposed algorithm in terms of initial convergence.



Item Type:Article
Date:November 2002
Date Type:Publication
Subjects:Computer
Divisions:College Of Engineering Sciences > Electrical Engineering Dept
Creators:Abrar, Shafayat and Zerguine, A. and Bettayeb, M.
ID Code:14125
Deposited By:KFUPM ePrints Admin
Deposited On:24 Jun 2008 16:23
Last Modified:12 Apr 2011 13:14

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