A generalized minimal residual based iterative back propagation algorithm for polynomial nonlinear models

In this paper, a back propagation algorithm is proposed for polynomial nonlinear models using generalized minimal residual method. This algorithm, based on Arnoldi’s method, can be regarded as a modified gradient descent iterative algorithm, and provides several advantages over the traditional gradi...

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Bibliographic Details
Published in:Systems & control letters Vol. 153; p. 104966
Main Authors: Chen, Jing, Rong, Yingjiao, Zhu, Quanmin, Chandra, Budi, Zhong, Hongxiu
Format: Journal Article
Language:English
Published: Elsevier B.V 01.07.2021
Subjects:
Arnoldi’s method
Back propagation
Convergence analysis
Gradient descent iterative algorithm
Nonlinear model
Back propagation
Gradient descent iterative algorithm
Nonlinear model
Arnoldi’s method
Convergence analysis
ISSN:0167-6911, 1872-7956
Online Access:Get full text
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Summary:In this paper, a back propagation algorithm is proposed for polynomial nonlinear models using generalized minimal residual method. This algorithm, based on Arnoldi’s method, can be regarded as a modified gradient descent iterative algorithm, and provides several advantages over the traditional gradient descent iterative algorithm: (1) has less computational efforts for systems with missing data/large-scale systems; (2) does not require the eigenvalue calculation in step-length design; (3) adaptively computes the step-length in each iteration. Therefore, it can be employed for large-scale system identification. The feasibility and effectiveness of the proposed algorithm are established in theory and demonstrated by two simulation examples.
ISSN:0167-6911
1872-7956
DOI:10.1016/j.sysconle.2021.104966