Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal. (17th February 2009)

• Record Type: Journal Article
• Authors: Mankar, V. R.; Ghatol, A. A.
• Other Names: Becerikli Yasar Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2009)
• Publication Date: 2009-02-17
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2009/942697
• Languages: English
• ISSNs: 1687-7594

Abstract

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.