(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
Subjects:	Computer
Department:	College of Engineering and Physics > Electrical Engineering
Depositing User:	Mr. Admin Admin
Date Deposited:	24 Jun 2008 13:23
Last Modified:	01 Nov 2019 14:04
URI:	http://eprints.kfupm.edu.sa/id/eprint/14125