The filtering based auxiliary model generalized extended stochastic gradient identification for a multivariate output-error system with autoregressive moving average noise using the multi-innovation theory-Reference-Cited by-同舟云学术

The filtering based auxiliary model generalized extended stochastic gradient identification for a multivariate output-error system with autoregressive moving average noise using the multi-innovation theory

Published:2020-06 Issue:9 Volume:357 Page:5591-5609
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Ding Feng^ORCID,Wan Lijuan,Guo Yunze,Chen Feiyan

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Reference82 articles.

1. Particle filtering based parameter estimation for systems with output-error type model structures;Ding;J. Frankl. Inst.,2019

2. Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output;Ding;IET Control Theory Appl.,2019

3. The innovation algorithms for multivariable state-space models;Ding;Int. J. Adapt. Control Signal Process.,2019

4. A regularized variable projection algorithm for separable nonlinear least-squares problems;Chen;IEEE Trans. Autom. Control,2019

5. Weighted parameter estimation for hammerstein nonlinear ARX systems;Ding;Circuits Syst. Signal Process.,2020

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