Jui, Julakha Jahan and Mohd Ashraf, Ahmad and Muhammad Ikram, Mohd Rashid (2022) Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey. Bulletin of Electrical Engineering and Informatics, 11 (1). pp. 454-465. ISSN 2089-3191. (Published)
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Abstract
Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Block oriented models; Hammerstein model; Metaheuristics; Nonlinear system identification; Optimization; Population-based optimization |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 15 Apr 2022 07:21 |
| Last Modified: | 15 Apr 2022 07:21 |
| URI: | http://umpir.ump.edu.my/id/eprint/33580 |
| Download Statistic: | View Download Statistics |
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