Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering

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• 作者：Feng Ding^a ; ^b ; ^c ; ^{fding@jiangnan.edu.cn} ; Feifei Wang^a ; ^{ffwang12@126.com} ; Ling Xu^a ; ^{lingxu0848@163.com} ; Minghu Wu^b ; ^{wuxx1005@163.com}
• 刊名：Journal of the Franklin Institute
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：354
• 期：3
• 页码：1321-1339
• 全文大小：561 K
• 卷排序：354

文摘

This paper develops a decomposition based least squares iterative identification algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering. The key is to apply the data filtering technique to transform the original system to a hierarchical identification model, and to decompose this model into three subsystems and to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm requires less computational efforts. The simulation results show that the proposed algorithms can work well.