Multi-direction gradient iterative algorithm: A unified framework for gradient iterative and least squares algorithms

Chen, Jing; Ma, Junxia; Gan, Min; Zhu, Quanmin

Abstract

In this study, a multi-direction-based gradient iterative (GI) algorithm for Hammerstein systems with irregular sampling data is proposed. The algorithm updates the parameter estimates using several orthogonal directions at each iteration. The convergence rate is significantly improved with an increasing number of directions. The convergence property and two simulation examples are provided to demonstrate the effectiveness of the proposed algorithm. In addition, the multi-direction-based GI algorithm establishes a relationship between the traditional GI and least squares (LS) algorithms. Thus, our algorithm that combines the LS and GI algorithms constructs an identification framework for a significantly wider class of systems.

• Journal Article Type: Article
• Acceptance Date: Nov 24, 2022
• Online Publication Date: Dec 2, 2021
• Publication Date: 2022-12
• Deposit Date: Dec 22, 2022
• Publicly Available Date: Dec 22, 2022
• Journal: IEEE Transactions on Automatic Control
• Print ISSN: 0018-9286
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 67
• Issue: 12
• Pages: 6770-6777
• DOI: https://doi.org/10.1109/TAC.2021.3132262
• Keywords: Electrical and Electronic Engineering, Computer Science Applications, Control and Systems Engineering, Convergence, Eigenvalues and eigenfunctions, Computational modeling, Mathematical models, Load modeling, Technological innovation, Iterative algorithms, Computational load, convergence rate, gradient iterative (GI) algorithm, Hammerstein system, irregular sampling, multidirection
• Public URL: https://uwe-repository.worktribe.com/output/10256644
• Publisher URL: https://ieeexplore.ieee.org/document/9633163