An Accelerated Parameter Identification Algorithm for Linear Regression Systems

In this paper, a parameter identification algorithm is proposed for linear regression systems with constant unknown parameters. Such an algorithm is composed of an accelerated version of the heavy–ball method and a modified extension of Kreisselmeier’s filters and can identify constant parameters in a finite time under a persistent excitation condition. The workability of the proposed algorithm is depicted by simulation results.