Feed-Forward Neural Network Blind Equalization Algorithm Based on Super-Exponential Iterative

Author

Gao, Min ; Guo, Ye-Cai ; Liu, Zhen-Xing ; Zhang, Yan-Ping

Author_Institution

Anhui Univ. of Sci. & Technol., Huainan, China

Volume

1

fYear

2009

fDate

26-27 Aug. 2009

Firstpage

335

Lastpage

338

Abstract

In order to overcome the slow convergence rate and larger mean square error of feed-forward neural network (FNN) blind equalization algorithm, a feed-forward neural network blind equalization algorithm based on super-exponential iterative (SEI) is proposed, on basis of the futures of super-exponential iterative and feed-forward neural network blind equalization algorithm. The proposed algorithm has ability to improve convergence rate and to reduce mean square error via full using the whiten ability of SEI. With underwater acoustic channels simulation results show that the proposed algorithm has outperformed feed-forward neural network (FNN) blind equalization algorithm in the convergence rate and mean square error.

Keywords

blind equalisers; feedforward neural nets; iterative methods; mean square error methods; telecommunication channels; telecommunication computing; underwater acoustic communication; blind equalization algorithm; feed-forward neural network; mean square error method; super-exponential iterative algorithm; underwater acoustic channel; Artificial neural networks; Blind equalizers; Convergence; Feedforward neural networks; Feedforward systems; Interference; Iterative algorithms; Mean square error methods; Neural networks; Underwater acoustics; Blind equalization; Feed-forward Neural Network; Super-Exponential Iterative; underwater acoustic channels;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Human-Machine Systems and Cybernetics, 2009. IHMSC '09. International Conference on

Conference_Location

Hangzhou, Zhejiang

Print_ISBN

978-0-7695-3752-8

Type

conf

DOI

10.1109/IHMSC.2009.92

Filename

5336153