Multi‐innovation gradient estimation algorithms for multivariate equation‐error autoregressive moving average systems based on the filtering technique.

This study concentrates on the parameter estimation of multivariate pseudo‐linear autoregressive moving average systems by means of the multi‐innovation identification theory and data filtering technique. A multi‐innovation stochastic gradient algorithm is derived by introducing the innovation length in the stochastic gradient algorithm. Then, the original system is transformed into two subsystems by using a filter. A filtering‐based multi‐innovation stochastic gradient algorithm is presented, whose parameter estimation accuracy is higher than the multi‐innovation stochastic gradient algorithm. The simulation results confirm that these two algorithms are effective. [ABSTRACT FROM AUTHOR]