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Second-order optimization methods for time-delay Autoregressive eXogenous models: Nature gradient descent method and its two modified methods

Chen, Jing; Pu, Yan; Guo, Liuxiao; Cao, Junfeng; Zhu, Quanmin

Second-order optimization methods for time-delay Autoregressive eXogenous models: Nature gradient descent method and its two modified methods Thumbnail


Authors

Jing Chen

Yan Pu

Liuxiao Guo

Junfeng Cao

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Quan Zhu Quan.Zhu@uwe.ac.uk
Professor in Control Systems



Abstract

This article proposes several second-order optimization methods for time-delay ARX model. Since the time-delay in the information vector makes the traditional identification algorithms be inefficient, a redundant rule based method is utilized to transformed the model into a redundant model. Then, the nature gradient descent (NGD) algorithm is developed for such a model. To reduce the computational efforts of the NGD algorithm and to adaptively update each element in the parameter vector, two modified NGD algorithms are also presented. The simulation examples verify the effectiveness of the proposed algorithms.

Journal Article Type Article
Acceptance Date Oct 8, 2022
Online Publication Date Oct 25, 2022
Publication Date Jan 1, 2023
Deposit Date Nov 8, 2022
Publicly Available Date Oct 26, 2023
Journal International Journal of Adaptive Control and Signal Processing
Print ISSN 0890-6327
Electronic ISSN 1099-1115
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 37
Issue 1
Pages 211-223
DOI https://doi.org/10.1002/acs.3519
Keywords Electrical and Electronic Engineering, Signal Processing, Control and Systems Engineering, ARX model, Time-delay, Nature gradient descent, Adaptive gradient descent, Momentum based method, Convergence rate
Public URL https://uwe-repository.worktribe.com/output/10121748
Publisher URL https://onlinelibrary.wiley.com/doi/10.1002/acs.3519

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