Design of adaptive filter using Jordan/Elman neural network in a typical EMG signal noise removal

Authors:
V. R. Mankar;A. A. Ghatol
Affiliations:
Electronics Department, Government Polytechnic, Amravati, India;Technological University, Lonere, Dist. Raigarh, India
Venue:
Advances in Artificial Neural Systems
Year:
2009

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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.