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

被引：9

作者：

Abrar, S ^{[1
]}

Zerguine, A

Bettayeb, M

机构：

[1] King Fahd Univ Petr & Minerals, Dept Comp Engn, Dhahran 31261, Saudi Arabia

[2] King Fahd Univ Petr & Minerals, Dept Elect Engn, Dhahran 31261, Saudi Arabia

[3] Univ Sharjah, Dept Elect & Elect Engn, Sharjah, U Arab Emirates

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2002年 / 13卷 / 06期

关键词：

complex-valued backpropagation algorithm; recursive least squares (RILS) algorithm; stop-and-go decision-directed (S&G-DD) blind equalization (BE) algorithm;

D O I：

10.1109/TNN.2002.804282

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1472 / 1481

页数：10

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