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Gradient-based iterative identification method for multivariate equation-error autoregressive moving average systems using the decomposition technique
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DOI：10.1016/j.jfranklin.2018.12.002.png)
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Abstract
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This paper studies the parameter estimation problems of multivariate equation-error autoregressive moving average systems. Firstly, a gradient-based iterative algorithm is presented as a comparison. In order to improve the computational efficiency and the parameter estimation accuracy, a decomposition-based gradient iterative algorithm is presented by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. Compared with the gradient-based iterative algorithm, the decomposition-based algorithm requires less computational efforts, and the simulation results indicate that this algorithm is effective. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
LEAST-SQUARES IDENTIFICATION
PARAMETER-ESTIMATION
NONLINEAR-SYSTEMS
ALGORITHM
DESIGN
PERFORMANCE
STATE
JUMP
LMS
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