A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm

Li, Yingsong; Cherednichenko, Aleksey; Jiang, Zhengxiong; Shi, Wanlu; Wu, Jinqiu

doi:10.3390/sym11050697

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…A comparison of heuristic algorithms for the identification of DC motor systems was carried out in [14] through discrete Proportional-Integral (PI) controllers to analyze the system response. Fast and accurate convergence rate results were obtained with the extended Kalman filter (EKF) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

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Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used.

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Section: Introductionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…However, the APSA is not particularly useful in sparse system identification as network echo cancellation or underwater acoustic channel estimation, in which the impulse response is primarily composed of near-zero coefficients and only a few large coefficients. In this context, many adaptive filters have been proposed to optimize filter performance by considering the system sparsity [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

ℒp-Norm-like Affine Projection Sign Algorithm for Sparse System to Ensure Robustness against Impulsive Noise

Shin

Kim

et al. 2021

Symmetry

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An improved affine projection sign algorithm (APSA) was developed herein using a ℒp-norm-like constraint to increase the convergence rate in sparse systems. The proposed APSA is robust against impulsive noise because APSA-type algorithms are generally based on the ℒ1-norm minimization of error signals. Moreover, the proposed algorithm can enhance the filter performance in terms of the convergence rate due to the implementation of the ℒp-norm-like constraint in sparse systems. Since a novel cost function of the proposed APSA was designed for maintaining the similar form of the original APSA, these have symmetric properties. According to the simulation results, the proposed APSA effectively enhances the filter performance in terms of the convergence rate of sparse system identification in the presence of impulsive noises compared to that achieved using the existing APSA-type algorithms.

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A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm

Cited by 2 publications

References 25 publications

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

ℒp-Norm-like Affine Projection Sign Algorithm for Sparse System to Ensure Robustness against Impulsive Noise

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