A New Recursive Identification Method for Weighted Criterion

Abstract

In time-series system modeling traditional criterions only consider current estimation error while the past and global prediction errors are usually overlooked. By integrating both the estimation error and the difference between neighbor prediction errors, a novel weighted identification criterion was presented. Based on this criterion the alternations of the two-step algorithm is constructed through separating the system parameters estimation and noise parameters estimation for the system disturbed by colored noise, which could result in oscillation and instability. An extended one-step recursive algorithm for the weighted identification criterion is introduced in this paper. For the input-output system disturbed by colored noise, the prediction gradients and the gradient of the pseudo linear regression vector are given. The gradient iterative algorithm and the direct adaptive method (DAM), the new one-step recursive algorithm are proposed by a series of estimation process optimizations. Finally, a simulation example is conducted to demonstrate the efficiency of this new method.