A constrained anti-Hebbian learning algorithm for total least-squares estimation with applications to adaptive FIR and IIR filtering

TLDR: In this paper, a new Hebbian-type learning algorithm for the total least squares parameter estimation is presented, which allows the weight vector of a linear neuron unit to converge to the eigenvector associated with the smallest eigenvalue of the correlation matrix of the input signal.

Abstract: In this paper, a new Hebbian-type learning algorithm for the total least-squares parameter estimation is presented. The algorithm is derived from the classical Hebbian rule. An asymptotic analysis is carried out to show that the algorithm allows the weight vector of a linear neuron unit to converge to the eigenvector associated with the smallest eigenvalue of the correlation matrix of the input signal. When the algorithm is applied to solve parameter estimation problems, the converged weights directly yield the total least-squares solution. Since the process of obtaining the estimate is optimal in the total least-squares sense, its noise rejection capability is superior to those of the least-squares-based algorithms. It is shown that the implementations of the proposed algorithm have the simplicity of those of the LMS algorithm. The applicability and performance of the algorithm are demonstrated through computer simulations of adaptive FIR and IIR parameter estimation problems. >