Hierarchical gradient based and hierarchical least squares based iterative parameter identification for CARARMA systems

According to the iterative identification technique and the hierarchical identification principle, this paper presents a two-stage gradient based and a least squares based iterative parameter estimation algorithms (i.e., the hierarchical gradient based iterative algorithm and the hierarchical least squares based iterative algorithm) for controlled autoregressive autoregressive moving average systems. The proposed two-stage least squares based iterative algorithm requires less computation compared with the least squares based iterative algorithm. The simulation results indicate that the two-stage least squares based iterative algorithm converges faster than the two-stage gradient based iterative algorithm. HighlightsThis paper presents a two-stage gradient based and a least squares based iterative estimation algorithms for controlled autoregressive ARMA systems.The two-stage least squares based iterative algorithm requires less computation compared with the least squares based iterative algorithm.The two-stage least squares based iterative algorithm converges faster than the two-stage gradient based iterative algorithm.

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