Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal

Advances in Artificial Neural Systems

Research Article

A. A. Ghatol¹  V. R. Mankar² 

[1]Technological University, Lonere, Dist. Raigarh, (M.S.), India

[2]Electronics Department, Government Polytechnic, Amravati, (M.S.) 444 604, India

Others  :  1374430
DOI  :  10.1155/2009/942697

 received in 2008-07-28, accepted in 2009-02-03,  发布年份 2009

【 摘 要 】

The bioelectric potentials associated with muscle activity constitute the electromyogram (EMG). These EMG signals are low-frequency and lower-magnitude signals. In this paper, it is presented that Jordan/Elman neural network can be effectively used for EMG signal noise removal, which is a typical nonlinear multivariable regression problem, as compared with other types of neural networks. Different neural network (NN) models with varying parameters were considered for the design of adaptive neural-network-based filter which is a typical SISO system. The performance parameters, that is, MSE, correlation coefficient, N/P, and t, are found to be in the expected range of values.

【 授权许可】

CC BY   
Copyright © 2009 2009

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