Advances in Artificial Neural Systems
Volume 2009 (2009), Article ID 942697, 9 pages
Research Article

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.


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.