Infinite impulse response system identification using average differential evolution algorithm with local search

Durmuş, Burhanettin

doi:10.1007/s00521-021-06399-4

Cited by 17 publications

(5 citation statements)

References 21 publications

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“…Some parameters have been provided in the previous section, such as E and rl . Finally, the position update of the golden jackal is computed as formula (17).…”

Section: Exploitation Phase or Enclosing And Pouncing The Preymentioning

confidence: 99%

“…Chang created a new Hammerstein model based on the IIR system and Volterra neural network to identify the nonlinear discrete system, and the algorithm verified the validity and reliability of the model [16]. Durmuş used a modified average differential evolution algorithm to accomplish the IIR system identification, this method had extensive optimization ability to generate better control parameters [17]. Kowalczyk et al published a new approach based on an IIR filter matrix to design hardware architecture, the approach found a quicker optimization frequency and superior calculation precision [18].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Zhang

Kong

et al. 2023

J Supercomput

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The significant error of the adaptive infinite impulse response (IIR) system identification often involves nonlinearity and indifferentiability, the practical and reliable swarm intelligence optimization techniques are required to calculate and establish the ideal parameters of the IIR filter. In this research, an enhanced golden jackal optimization (EGJO) based entirely on the elite opposition-based learning technique and the simplex technique can be adopted to address this issue.The intention is to minimize the error fitness value and attain the appropriate control parameters. The golden jackal optimization (GJO), based on the cooperative attacking behavior of the golden jackals, simulates the searching for prey, stalking and enclosing prey, pouncing prey to efficaciously tackle the complicated optimization problem. The elite opposition-based learning technique has the characteristics of boosting population diversity, enhancing exploration ability, extending search range and avoiding search stagnation. The simplex technique has the characteristics of accelerating the search process, enhancing the exploitation ability, improving the computational precision and increasing the optimization depth. The EGJO can realize the balance between exploration and exploitation to arrive at the best possible outcome. To demonstrate the overall search ability, the EGJO is compared with those of the AOA, GTO, HHO, MDWA, RSO, WOA, TSA and GJO by gradually decreasing the error fitness value of the IIR filter. The experimental results clearly demonstrate that the optimization efficiency and recognition accuracy of EGJO are superior to those of other algorithms. The EGJO offers several benefits to obtaining a faster convergence rate, higher computation precision, better control parameters and better fitness value. In addition, the EGJO is very stable and resilient in tackling the IIR system identification problem.

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“…Some parameters have been provided in the previous section, such as E and rl . Finally, the position update of the golden jackal is computed as formula (17).…”

Section: Exploitation Phase or Enclosing And Pouncing The Preymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Zhang

Kong

et al. 2023

J Supercomput

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show abstract

“…The appropriate filter coefficients are searched by an adaptive algorithm in IIR filter design so that the output of the respective filter can be as close to an unknown system as possible. Figure 2 The IIR system's input-output relationship can be expressed as follows (Durmuş, 2022;Karaboga, 2009) where the input and the output of the filter are represented by 𝑥(𝑛) and 𝑦(𝑛), respectively.…”

Section: Adaptive Iir Filter Modelmentioning

confidence: 99%

“…The metaheuristic optimizers have recently been used as promising candidates to deal with IIR modeling since such optimizers have so far shown to reach more accurate and robust results (Eswari et al, 2021). Therefore, different metaheuristic algorithm examples such as cat swarm optimization (Panda et al, 2011), harmony search algorithm (Saha et al, 2014), bat algorithm (Kumar et al, 2016), selfish herd optimization (Zhao et al, 2020) and average differential evolution algorithm (Durmuş, 2022) can be found in the literature for IIR system identification.…”

Section: Introductionmentioning

confidence: 99%

Application of Slime Mould Algorithm to Infinite Impulse Response System Identification Problem

İzci

Ekinci

GÜLEYDİN

2022

BBD

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Recently, the researchers working in the field of science and engineering have paid a considerable attention to the concept of the system identification to tackle with complex optimization problems. It is feasible to achieve more accurate models of physical plants with the infinite impulse response (IIR) models compared to their finite counterparts (FIR). To get the most out of the IIR models for the system identification, metaheuristic optimization algorithms can be used as efficient solutions. This work, therefore, aims to demonstrate more promising performance of a new metaheuristic algorithm named slime mould algorithm. In this regard, a comparative assessment is performed using different metaheuristic optimization techniques and different IIR model identification problems are considered. The slime mould algorithm is shown to achieve better accuracy and robustness in terms of IIR model identification with the help of obtained statistical results.

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Section: Introductionmentioning

confidence: 99%

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Zhang

Kong

et al. 2022

Preprint

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The significant error of the adaptive infinite impulse response (IIR) system identification often involves nonlinearity and indifferentiability, the practical and reliable swarm intelligence optimization techniques are required to calculate and establish the ideal parameters of the IIR filter. In this research, an enhanced golden jackal optimization (EGJO) based entirely on the elite opposition-based learning technique and the simplex technique can be adopted to address this issue. The intention is to minimize the error fitness value and attain the appropriate control parameters. The golden jackal optimization (GJO), based on the cooperative attacking behavior of the golden jackals, simulates the searching for prey, stalking and enclosing prey, pouncing prey to efficaciously tackle the complicated optimization problem. The elite opposition-based learning technique has the characteristics of boosting population diversity, enhancing exploration ability, extending search range and avoiding search stagnation. The simplex technique has the characteristics of accelerating the search process, enhancing the exploitation ability, improving the computational precision and increasing the optimization depth. The EGJO can realize the balance between exploration and exploitation to arrive at the best possible outcome. To demonstrate the overall search ability, the EGJO is compared with those of the AOA, GTO, HHO, MDWA, RSO, WOA, TSA and GJO by gradually decreasing the error fitness value of the IIR filter. The experimental results clearly demonstrate that the optimization efficiency and recognition accuracy of EGJO are superior to those of other algorithms. The EGJO offers several benefits to obtaining a faster convergence rate, higher computation precision, better control parameters and better fitness value. In addition, the EGJO is very stable and resilient in tackling the IIR system identification problem.

show abstract

Infinite impulse response system identification using average differential evolution algorithm with local search

Cited by 17 publications

References 21 publications

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Application of Slime Mould Algorithm to Infinite Impulse Response System Identification Problem

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

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