Non-quadratic recursive algorithms (RLK) for transversal plant identification

Abstract

A generalization of the RLS algorithm is presented. The objective measure to be minimized is composed of the sum of arbitrarily weighted kth powers of the observed error (RLK algorithm). The authors formulate general recursive algorithm in the context of noisy transversal plant identification. An approximate analysis of its performance based on the convergence of the mean and covariance matrix of the adaptive filter coefficients is carried out. This analysis evidences the importance of the choice of the order k under the knowledge of the plant noise statistics. The coherence of some computer simulation results for two different algorithms (k=2, 4) and plant noise statistics (binary and Laplacian) with the theoretical analysis is shown.<>