Convergence properties of the least squares estimation algorithm for multivariable systems

AbstractThis paper focuses on the convergence properties of the least squares parameter estimation algorithm for multivariable systems that can be parameterized into a class of multivariate linear regression models. The performance analysis of the algorithm by using the stochastic process theory and the martingale convergence theorem indicates that the parameter estimation errors converge to zero under weak conditions. The simulation results validate the proposed theorem