An improved Particle Swarm optimization algorithm towards IIR system identification

Hammou, Farid; Hammouche, Kamal

doi:10.1109/ccssp49278.2020.9151495

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…e third is to improve the existing algorithms by introducing new mechanics or strategies. Hammou et al [33] improved the particle swarm optimization algorithm with a strategy based on cooperation and hierarchization concepts for the updating of the best personal positions of particles. In recent years, metaheuristic algorithms are used in grey models for finding the optimal parameter solutions.…”

Section: Introductionmentioning

confidence: 99%

An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

Jiang

Feng

Liu

2021

Mathematical Problems in Engineering

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In order to improve the prediction performance of the existing nonlinear grey Bernoulli model and extend its applicable range, an improved nonlinear grey Bernoulli model is presented by using a grey modeling technique and optimization methods. First, the traditional whitening equation of nonlinear grey Bernoulli model is transformed into its linear formulae. Second, improved structural parameters of the model are proposed to eliminate the inherent error caused by the leap jumping from the differential equation to the difference one. As a result, an improved nonlinear grey Bernoulli model is obtained. Finally, the structural parameters of the model are calculated by the whale optimization algorithm. The numerical results of several examples show that the presented model’s prediction accuracy is higher than that of the existing models, and the proposed model is more suitable for these practical cases.

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Section: Introductionmentioning

confidence: 99%

An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

Jiang

Feng

Liu

2021

Mathematical Problems in Engineering

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Inclined planes system optimization: Theory, literature review, and state-of-the-art versions for IIR system identification

Mohammadi

Sheikholeslam

Mirjalili

2022

Expert Systems with Applications

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Adaptive Infinite Impulse Response System Identification Using Elitist Teaching-Learning- Based Optimization Algorithm

Ramalakshmanna

Shanmugaraja

Raju³

et al. 2023

International Journal of Circuits, Systems and Signal Processing

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Infinite Impulse Response (IIR) systems identification is complicated by traditional learning approaches. When reduced-order adaptive models are utilised for such identification, the performance suffers dramatically. The IIR system is identified as an optimization issue in this study. For system identification challenges, a novel population-based technique known as Elitist teacher learner-based optimization (ETLBO) is used to calculate the best coefficients of unknown infinite impulse response (IIR) systems. The MSE function is minimised and the optimal coefficients of an unknown IIR system are found in the system identification problem. The MSE is the difference between an adaptive IIR system's outputs and an unknown IIR system's outputs. For the unknown system coefficients of the same order and decreased order cases, exhaustive simulations have been performed. In terms of mean square error, convergence speed, and coefficient estimation, the results of actual and reduced-order identification for the standard system using the novel method outperform state-of-the-art techniques. For approximating the same-order and reduced-order IIR systems, four benchmark functions are examined utilizing GA, PSO, CSO, and BA. To demonstrate the improvements, the approach is evaluated on three conventional IIR systems of 2nd, 3rd, and 4th order models. On the basis of computing the mean square error (MSE) and fitness function, the suggested ETLBO approach for system identification is proven to be the best among others. Furthermore, it is confirmed that the suggested ETLBO method outperforms some of the other known system identification strategies. Finally, the efficiency of the dynamic nature of the control parameters of DE, TLBO, and BA in finding near parameter values of unknown systems is demonstrated through comparison data. The simulation results show that the suggested system identification approach outperforms the current methods for system identification.

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An improved Particle Swarm optimization algorithm towards IIR system identification

Cited by 3 publications

References 8 publications

An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

Inclined planes system optimization: Theory, literature review, and state-of-the-art versions for IIR system identification

Adaptive Infinite Impulse Response System Identification Using Elitist Teaching-Learning- Based Optimization Algorithm

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