Abstract
A modern identification algorithm to reduce the complexity of estimating parameters for discrete time-invariant linear systems and nonlinear systems is presented. The algorithm requires no a priori knowledge of the input or of the order of the system. An identification unbiased estimator method is presented which reduces the computational complexity of covariance matrix inversion. Probability one convergence of the estimated parameters to their true values is presented, and stability of the identification algorithm is discussed. An example is presented to illustrate the results.