MISO Nonlinear Gauss-Hammerstein Model Identification of Ultrasonic Motor

Huang, Wenwen; Jingzhuo, Shi

doi:10.1007/s40313-020-00676-8

Cited by 2 publications

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“…They claim that the DE method outperforms other metaheuristics such as GAS, PSO, pattern search (PS) and simulated annealing (SA). Recently, another real plant (ultrasonic motor) is modelled with the Hammerstein system and the DE algorithm is used to identify the model parameters [69]. A timeline of studies (2011-2021) involving the Hammerstein model incorporating evolutionary computing and swarm algorithms is given in Figure 5.…”

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confidence: 99%

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

2022

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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.

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confidence: 99%