System identification using Hammerstein model optimized with differential evolution algorithm

Mete, Selcuk; Özer, Şaban; Zorlu, Hasan

doi:10.1016/j.aeue.2016.10.005

Cited by 37 publications

(14 citation statements)

References 30 publications

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“…Differential Evolution Algorithm (DEA) is verified to be an efficient method for solving optimization problems in literature [20][21][22][23][24][25][26][27][28][29]. Basically, DEA has six important parameters as population size (NP), generation number (GN), combination factor (CF), scaling factor (F), upper and low bounds (ULB), crossover rate (C).…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

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Optimization of weighted myriad filters with differential evolution algorithm

Zorlu

2017

AEU - International Journal of Electronics and Communications

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Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…Also DEA is easily implemented by computer programs. So DEA is successfully applied to many different fields and system optimization problems [22][23][24][25][26][27][28][29][30]. But DEA has several disadvantages such as unstable convergence in the last period and easy to drop into local minimum [29].…”

Section: Introductionmentioning

confidence: 99%

Optimization of weighted myriad filters with differential evolution algorithm

Zorlu

2017

AEU - International Journal of Electronics and Communications

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“…In the literature, it has been involved in a lot of work related to modelling in recent years [1]- [10]. Mete et al [5] presented system modelling based on cascade of a nonlinear second order volterra (SOV) model and a linear FIR model using DE algorithms. Another study which is examination of ABC algorithm performance in the modelling of higher order systems is presented by Bagis and Senberber [4].…”

Section: Introductionmentioning

confidence: 99%

ABC Algorithm Based System Modelling for Tuning the Fractional Order PID Controllers

Senberber¹,

Bağış²

2018

ElAEE

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The effectiveness of the model structure for designing control system is highly depending on the right selection of tuning parameters belonging of the algorithms. No doubt, preferable parameter estimation leads to better results for the modelling process. This study consists of two main parts. In the first part, fractional and integer model structures with time delay are proposed for integer-order systems using artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm. The second part of the study is the fractional order PID controller design based on the use of new models obtained with the help of Matlab/Simulink software package. While the integrated square error (ISE) is preferred in the modelling process as the performance criterion, four different performance indices are chosen as ISE, integral time-square error (ITSE), integral absolute error (IAE) and integral timeweighted absolute error (ITAE) during the controller design phase. It is shown that, the results obtained with fractional modelling have achieved better results than the integer order modelling. Furthermore, the controller designs for the algorithm based models proposed in the first stage of the study present a satisfactory performance.

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“…In this example, considering the structure given in Figure 6, nonlinear system, which is a Bilinear [57,62,63], is chosen as in Equation (18). It is identified with four different types of model.…”

Section: Example-imentioning

confidence: 99%