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2013 (Vol. 5, Issue: 02)
Article Information:

Instantaneous Gradient Based Dual Mode Feed-Forward Neural Network Blind Equalization Algorithm

Ying Xiao
Corresponding Author:  Ying Xiao 

Key words:  Blind equalization, constant modulus algorithm, dual mode algorithm, feed-forward neural network, instantaneous gradient, ,
Vol. 5 , (02): 671-675
Submitted Accepted Published
May 30, 2012 June 23, 2012 January 11, 2013
Abstract:

To further improve the performance of feed-forward neural network blind equalization based on Constant Modulus Algorithm (CMA) cost function, an instantaneous gradient based dual mode between Modified Constant Modulus Algorithm (MCMA) and Decision Directed (DD) algorithm was proposed. The neural network weights change quantity of the adjacent iterative process is defined as instantaneous gradient. After the network converges, the weights of neural network to achieve a stable energy state and the instantaneous gradient would be zero. Therefore dual mode algorithm can be realized by criterion which set according to the instantaneous gradient. Computer simulation results show that the dual mode feed-forward neural network blind equalization algorithm proposed in this study improves the convergence rate and convergence precision effectively, at the same time, has good restart and tracking ability under channel burst interference condition.
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  Cite this Reference:
Ying Xiao, 2013. Instantaneous Gradient Based Dual Mode Feed-Forward Neural Network Blind Equalization Algorithm.  Research Journal of Applied Sciences, Engineering and Technology, 5(02): 671-675.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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