Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

Abstract

Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.

Keywords

• adaptive filters
• dichotomous coordinate descent (DCD)
• recursive least-squares (RLS)
• system identification
• tensor decomposition