Title :
Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization
Author :
Abrar, Shafayat ; Zerguine, Azzedine ; Bettayeb, Maamar
Author_Institution :
Dept. of Comput. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
fDate :
11/1/2002 12:00:00 AM
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.
Keywords :
backpropagation; blind equalisers; convergence of numerical methods; feedforward neural nets; least squares approximations; multilayer perceptrons; convergence; data communication systems; feedforward neural network; intersymbol-interference cancelling; learning; mean-square error; multilayer neural network; recursive least-squares backpropagation algorithm; simulation; stop-and-go decision-directed blind equalization; Backpropagation algorithms; Blind equalizers; Convergence; Data communication; Decision feedback equalizers; Feedforward neural networks; Least squares methods; Multi-layer neural network; Neural networks; Robustness;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.804282
