Improve Recursive Identification using Multi-Iteration

A multi-iteration recursive identification algorithm is proposed and studied. The algorithm performs multiple iterations for each data sample in order to improve the accuracy of the estimate. The algorithm is worked out for an ARMAX model using Gauss Newton method and pseudo-linear regression. The convergence of the methods is proved by using stochastic process theory and martingale convergence technique. Simulation results show that the algorithm has higher accuracy in parameter estimation and faster convergence than the conventional recursive method.