A comparison study of system identification using Hammerstein model

Özer, Şaban; Zorlu, Hasan; Mete, Selcuk

doi:10.1109/innovations.2015.7381569

Cited by 3 publications

(5 citation statements)

References 33 publications

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“…Next, the Hammerstein model with GA managed to further decrease CPU time and MSE, when compared to GA alone, DE, and PSO [57]. Upon comparing two models of Hammerstein trained using DE, CSA, RLS, and GA; MPN with finite impulse response (FIR) filter (MPN-FIR) and volterra second-order with FIR (SOV-FIR), DE offered the best outcomes [58]. Using the Hammerstein model, the PSO and ISSN: 2302-9285  Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey (Julakha Jahan Jui)…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the study made by Vesterstrom and Thomsen [67] claimed that DE is better than PSO and EAs as it can obtain the lowest fitness solution for most of the problems reported in the literature. It was again reported that the convergence speed of DE is significantly faster than GA [58]. Additionally, DE is robust as it can consistently reproduce the same solutions over many simulations compared to PSO.…”

mentioning

confidence: 92%

“…Next, the Hammerstein model with GA managed to further decrease the CPU time and MSE, when compared to GA alone, DE, and PSO [57]. Upon comparing the two models of the trained Hammerstein using DE, CSA, RLS, and GA, MPN with finite impulse response (FIR) filter (MPN-FIR) and volterra second-order with FIR (SOV-FIR), DE offered the best outcomes [58]. In the study, it was found that DE required fewer control parameters compared to a well-known method, particle swarm optimization (PSO).…”

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confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

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confidence: 92%

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confidence: 99%

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Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

2022

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“…In this example, considering the structure given in Figure 4 and 5 unknown ARMA system [45,46] is chosen as in Eq. (15).…”

Section: Example-iiimentioning

confidence: 99%

A New Hybrid Model Based On Neuro Fuzzy Network Soft Switching Mechanism For System Identification

Mete¹,

Zorlu²,

Özer³

2019

Bilişim Teknolojileri Dergisi

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This paper aims to improve a new hybrid model for system identification area. The proposed hybrid model consists of an adaptive Hammerstein model, an adaptive Wiener model, and a Neuro-Fuzzy (NF) network based softswitching mechanism (SSM). SSM structure in hybrid model increases the success of block model by selecting the best results of Hammerstein and Wiener model outputs. In literature, there are various studies about NF based on Hammerstein or Wiener model types applied to system identification. In the proposed model, Hammerstein and Wiener models with NF network are used together different from the literature. In simulation studies, five different type of systems are identified with different models (Hammerstein, Wiener and the proposed hybrid model) optimized by Recursive Least Square (RLS). Then the performances of these models are compared. Simulation results reveal the effectiveness and robustness of the proposed identification model. Özet-Bu çalışmanın amacı sistem kimliklendirme alanında yeni bir karma model geliştirmektir. Önerilen karma model uyarlanabilen bir Hammerstein model, bir Wiener model ve esnek anahtarlama mekanizmasına dayanan bulanık sinir ağını içermektedir. Karma modeldeki esnek anahtarlama mekanizması Hammerstein ve Wiener model çıkışlarının en iyi sonuçlarını seçerek blok model başarısını arttırmaktadır. Literatürde, sistem kimliklendirmede uygulanan bulanık sinir ağı temelli Hammerstein ya da Wiener modellerle ilgili birçok çalışma vardır. Önerilen modelde, bulanık sinir ağıyla birlikte Hammerstein ve Wiener modelleri literatürden farklı olarak bir arada kullanılmıştır. Simülasyon çalışmalarında, farklı tipteki beş sistem tekrarlayan en küçük kare ile optimize edilmiş olan farklı modeller (Hammerstein, Wiener ve önerilen model) ile kimliklendirilmiştir. Daha sonra bu modellerin performansları karşılaştırılmıştır. Simülasyon çalışmaları önerilen modelin etkinliğini ve sağlamlığını ortaya koymaktadır. Anahtar Kelimeler-bulanık sinir ağı, karma zeki sistemler, optimizasyon, esnek anahtarlama, sistem kimliklendirme

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“…The authors have reported better MSE and computational time over the algorithms compared, even for a coloured noise. In (Pal et al, 2015a), GSA was used for Hammerstein system identification and was shown to provide lower mean square error and lesser computational timer over PSO, DE and GA. (Ozer et al, 2015) compared the performance of two Hammerstein models, namely a second-order Volterra followed by an FIR filter (SOV-FIR) and a memoryless polynomial nonlinearity (MPN) followed by an FIR filter (MPN-FIR), trained using CSA, GA, DE, and RLS. SOV-FIR trained using DE was shown to offer the best performance.…”

Section: Hammerstein Model Identificationmentioning

confidence: 99%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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A comparison study of system identification using Hammerstein model

Cited by 3 publications

References 33 publications

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

A New Hybrid Model Based On Neuro Fuzzy Network Soft Switching Mechanism For System Identification

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

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