An Auxiliary-Model-Based Stochastic Gradient Algorithm for Dual-Rate Sampled-Data Box-Jenkins Systems

摘要

This paper presents an identification algorithm for Box-Jenkins systems by combining the auxiliary model identification idea and the gradient search principle. The proposed algorithm can estimate all unknown parameters of the Box-Jenkins systems. Furthermore, to improve the convergence rate of the stochastic gradient algorithm, a modified stochastic gradient algorithm is given. The simulation results indicate that the proposed algorithm can work well.