Intelligent instrument for tracking and adaptive filtering of oscillatory signals using Hebbian learning rules

Abstract

In this paper Hebbian type of learning algorithms using total least squares method is applied for adaptive filtering techniques to remove the noise and undesired oscillatory signals at different systems. Here we have used the generalized Hebbian learning rules for initializing the internal representations of a feedforward neural network, which accelerates the convergence of supervised Hebbian learning rule. In case of constrained anti-Hebbian learning rule, the weight vectors of linear neuron unit is converged to an eigenvector which has the smallest eigenvalue. In the total least squares (TLS) method the noise rejection capability is superior to the least squares method. Here we have applied the initial sets of data for the internal representation of feedforward network which consists of bottom-up unsupervised learning process followed by top-down supervised learning process using total least squares (TLS) algorithm. For faster convergence we have included the momentum term for the updating of weights. An intelligent instrumentation scheme has been developed for on-line measurement of amplitude of oscillatory signals. The undesired oscillations of the signal is also removed by implementing neural network model (using Hebbian rules and total least square algorithm) on a digital signal processor.