A Time-Domain Fractional Approach for Wiener-Hammerstein Systems Identification
Open accessAbstract
This paper describes a new approach to initialize Wiener-Hammerstein models for iterative prediction error minimization. The key idea is to parameterize the division of poles and zeros of the best linear approximation (BLA), between the two linear subsystems. Taylor expansion is used to handle the parameterization in the time-domain. Results regarding uniqueness of the initial estimate are proved on a low order example. The initial estimate generated with this approach allows to avoid problems with local minima and it is used for the final optimization in all model parameters.
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Giordano and Sjöberg [22] reviewed and extended the method in [21] using a time-domain approach to avoid the local minima problem.
“Giordano and Sjöberg [22] reviewed and extended this method using the time-domain approach to avoid the local minima problem.”
From Application of Fractional Calculus for Parameter Estimation of Nonlinear Wiener Systems With Time Delay (2024) · cites this paper as [22]
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Kothari, K. (2024) · IEEE Access · doi:10.1109/access.2024.3367441 · Cited by 10 · see citing works
“Giordano and Sjöberg [22] reviewed and extended this method using the time-domain approach to avoid the local minima problem.”
Method
- Authors
- Giordano, G., & Sjöberg, J.
- Affiliations
- Chalmers University of Technology (SE)
- Corresponding author
- Giordano, G.
- ORCID
- Giordano, G.
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2015
- Volume
- 48
- Issue
- 28
- Pages
- 1232-1237
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W2339771004
- Cited by
- 11
- References
- 0
- Citations by year
- 2024: 1 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 3 · 2016: 2
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- Chalmers Publication Library (Chalmers University of Technology) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Design
- Physical Sciences › Engineering › Computational Mechanics › Advanced Adaptive Filtering Techniques
- Keywords
- Wiener-Hammerstein systems, system identification, prediction error minimization, preliminary estimates, best linear approximation, Taylor series expansion, local optima avoidance, nonlinear system modeling
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