Blind Equalization by Neural Network Based on RPROP Algorithm

被引：0

作者：

Xiao Ying ^{[1
]}

Xu Hong-Zhou ^{[2
]}

机构：

[1] Dalian Nationality Univ, Coll Electromech & Informat Engn, Dalian, Liaoning, Peoples R China

[2] Chinese Peoples Liberat Army, Unit 91550, Element 94, Dalian, Liaoning, Peoples R China

来源：

2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31 | 2008年

关键词：

neural network; blind equalization; compressed transfer function; RPROP algorithm;

D O I：

暂无

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Blind equalization by neural network has two difficult problems, which is convergence rate and computational complexity. Resilient BP algorithm (RPROP) combining compressed transfer function is proposed to Improve blind equalization by neural network. Compressed transfer function can make the Input signal avoid saturation zone and RPROP algorithm can improve convergence rate effectively without adding additional calculation amount. The effectiveness of the algorithm is identified by simulation.

引用

页码：2044 / +

页数：2

共 7 条

[1]

Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, P303, DOI 10.1007/BF02551274

[2]

FAN MY, 2000, STUDY SIMULATION APP

[3] Blind equalization using the constant modulus criterion: A review [J].

Johnson, CR ;

Schniter, P ;

Endres, TJ ;

Behm, JD ;

Brown, DR ;

Casas, RA .

PROCEEDINGS OF THE IEEE, 1998, 86 (10) :1927-1950

[4] USING RECURRENT NEURAL NETWORKS FOR ADAPTIVE COMMUNICATION CHANNEL EQUALIZATION [J].

KECHRIOTIS, G ;

ZERVAS, E ;

MANOLAKOS, ES .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (02) :267-278

[5]

Sun Li-jun, 2006, Journal of System Simulation, V18, P431

[6]

XIAO Y, 2005, TECHNICAL ACOUSTICS, V24, P129

[7]

[肖瑛 XIAO Ying], 2006, [应用声学, Applied Acoustics], V25, P340

← 1 →