Improved gradient descent algorithms for time-delay rational state-space systems: Intelligent search method and momentum method

Chen, Jing; Zhu, Quanmin; Hu, Manfeng; Guo, Liuxiao; Narayan, Pritesh

Abstract

This study proposes two improved gradient descent parameter estimation algorithms for rational state-space models with time-delay. These two algorithms, based on intelligent search method and momentum method, can simultaneously estimate the time-delay and parameters without the matrix eigenvalue calculation in each iteration. Compared with the traditional gradient descent algorithm, the improved algorithms come with two advantages: having quicker convergence rates and less computational efforts, particularly meaningful for those large scale systems. A simulated example is selected to illustrate the efficiency of the proposed algorithms.

Citation

Chen, J., Zhu, Q., Hu, M., Guo, L., & Narayan, P. (2020). Improved gradient descent algorithms for time-delay rational state-space systems: Intelligent search method and momentum method. Nonlinear Dynamics, 101, 361-373. https://doi.org/10.1007/s11071-020-05755-8

• Journal Article Type: Article
• Acceptance Date: Jun 10, 2020
• Online Publication Date: Jun 20, 2020
• Publication Date: Jun 20, 2020
• Deposit Date: Jun 10, 2020
• Publicly Available Date: Jun 21, 2021
• Journal: Nonlinear Dynamics
• Print ISSN: 0924-090X
• Electronic ISSN: 1573-269X
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 101
• Pages: 361-373
• DOI: https://doi.org/10.1007/s11071-020-05755-8
• Public URL: https://uwe-repository.worktribe.com/output/6017182