A fixed-time convergent and noise-tolerant zeroing neural network for online solution of time-varying matrix inversion

Zhu, Jingcan; Zhao, Liang; Chen, Lei

doi:10.1016/j.asoc.2022.109691

Cited by 16 publications

(2 citation statements)

References 38 publications

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“…However, the denoising performance of Wiener filter is not ideal for ECG signals, as the ECG signal is non-stationary. Adaptive filtering makes the denoised ECG signal close to the reference signal by minimizing the mean square error ( 10 , 11 ). It has been used to suppress motion artifact, electromyogram, power line interference, and baseline wander.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme

Bing,

Liu,

Zhai

et al. 2024

Front. Cardiovasc. Med.

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BackgroundElectrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis.MethodsIn this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity.ResultsThe proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD).ConclusionsThe proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.

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Section: Introductionmentioning

confidence: 99%

A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme

Bing,

Liu,

Zhai

et al. 2024

Front. Cardiovasc. Med.

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“…Moreover, the problems of dynamic coupling, dynamic limitations caused by the environments, and delay problems of the controller are also should be considered, and they complicate the control process of manipulator trajectory tracking. Therefore, researchers have proposed the PID control [9,10], feedback control [11], finitetime control [12][13][14], fuzzy control [15][16][17][18] and neural network control [19][20][21][22][23][24][25] to solve the above problems.…”

Section: Introductionmentioning

confidence: 99%

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

Lan

Liu²

2023

Front. Phys.

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As a powerful method for time-varying problems solving, the zeroing neural network (ZNN) is widely applied in many practical applications that can be modeled as time-varying linear matrix equations (TVLME). Generally, existing ZNN models solve these TVLME problems in the ideal no noise situation without inequality constraints, but the TVLME with noises and inequality constraints are rarely considered. Therefore, a non-linear activation function is designed, and based on the non-linear activation function, a non-linearly activated ZNN (NAZNN) model is proposed for solving constrained TVLME (CTVLME) problems. The convergence and robustness of the proposed NAZNN model are verified theoretically, and simulation results further demonstrate the effectiveness and superiority of the NAZNN model in dealing with CTVLME and the constrained robot manipulator trajectory tracking problems. In addition, the wheeled robot trajectory tracking fault problems with physical constraints are also analyzed theoretically, and the proposed NAZNN model is also applied to the manipulator trajectory tracking fault problem, and the experimental results prove that the NAZNN model also deal with the manipulator trajectory tracking fault problem effectively.

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Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding

Luo,

Xiao,

Tan

et al. 2023

Communications in Computer and Information Science

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A fixed-time convergent and noise-tolerant zeroing neural network for online solution of time-varying matrix inversion

Cited by 16 publications

References 38 publications

A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme

A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding

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