Abstract
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Article Information:
Instantaneous Gradient Based Dual Mode Feed-Forward Neural Network Blind Equalization Algorithm
Ying Xiao
Corresponding Author: Ying Xiao
Submitted: May 30, 2012
Accepted: June 23, 2012
Published: January 11, 2013 |
Abstract:
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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.
Key words: Blind equalization, constant modulus algorithm, dual mode algorithm, feed-forward neural network, instantaneous gradient, ,
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Abstract
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Cite this Reference:
Ying Xiao, . Instantaneous Gradient Based Dual Mode Feed-Forward Neural Network Blind Equalization Algorithm. Research Journal of Applied Sciences, Engineering and Technology, (02): 671-675.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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