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

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.

Automated classification techniques of galaxies using artificial neural networks based classifiers

“…PCA is generally used to reduce the dimensionality of a dataset while retaining as much information as possible. Instead of using all the PCs of the covariance matrix, the data can be represented in terms of only a few basis vectors [7][8][9][10][11].…”

“…In the Elman network, the activities of the first hidden PEs are copied to the context units, while the Jordan network copies the output of the network. Networks which feed the input and the last hidden layer to the context units are also available [10].…”