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Hierarchical Extended Least Squares Estimation Approaches for a Multi-input Multi-output Stochastic System with Colored Noise from Observation Data by Ding, Feng; Zhang, Xiao; Lu, Xian; Zhan, Xi-Sheng; Alsaedi, Ahmed; Hayat, Tasawar is a Engineering article available to read on EtoBox.

What is Hierarchical Extended Least Squares Estimation Approaches for a Multi-input Multi-output Stochastic System with Colored Noise from Observation Data about?

This paper uses the hierarchical identification principle to decompose a multi-input multi-output equation-error moving average system into two subsystems, and then proposes a decomposition-based recursive extended least squares (D-RELS) algorithm (i.e., hierarchical extended least squares algorithm) to estimate the parameter matrices of these two subsystems. Before that, a recursive extended least squares (RELS) algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can estimate the system parameters effectively but the D-RELS algorithm has less computational cost than the RELS algorithm. The proposed D-RELS algorithm is extended to multi-input multi-output equation-error autoregressive moving average systems. The numerical simulation results demonstrate the effectiveness of the proposed algorithms.

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Author

Ding, Feng; Zhang, Xiao; Lu, Xian; Zhan, Xi-Sheng; Alsaedi, Ahmed; Hayat, Tasawar

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0016-0032)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)