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Advances in Artificial Neural Systems
Volume 2009 (2009), Article ID 942697, 9 pages
doi:10.1155/2009/942697
Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal
1Electronics Department, Government Polytechnic, Amravati, (M.S.) 444 604, India
2Technological University, Lonere, Dist. Raigarh, (M.S.), India
Received 28 July 2008; Revised 24 November 2008; Accepted 3 February 2009
Academic Editor: Yasar Becerikli
Copyright © 2009 V. R. Mankar and A. A. Ghatol. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
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.