A Variable Step-Size Robust Saturation Algorithm Against Impulsive Noises

Hur, Junwoong; Lee, Minho; Kim, Dongwoo; Park, PooGyeon

doi:10.1109/tcsii.2019.2954918

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Case 3. When ϵ + α = 0, based on Equation (37), there exists a very small constant β ∈ (0, − ϵ) which, due to the continuity of (37), makes the following hold…”

Section: Synchronisation Analysis Based On Prsmentioning

confidence: 99%

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The Synchronisation Problem of Chaotic Neural Networks Based on Saturation Impulsive Control and Intermittent Control

Cao,

Li,

Leung

2024

Mathematics

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This paper primarily focuses on the chaos synchronisation analysis of neural networks (NNs) under a hybrid controller. Firstly, we design a suitable hybrid controller with saturated impulse control, combined with time-dependent intermittent control. Both controls are low-energy consumption and discrete, aligning well with industrial development needs. Secondly, the saturation function in the chaotic neural network is addressed using the polyhedral representation method and the sector nonlinearity method, respectively. By integrating the Lyapunov stability theory, Jensen’s inequality, the mathematical induction method, and the inequality reduction technique, we establish suitable time-dependent Lyapunov generalised equations. This leads to the estimation of the domain of attraction and the derivation of local exponential stability conditions for the error system. The validity of the achieved theoretical criteria is eventually demonstrated through numerical experiment simulations.

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“…Case 3. When ϵ + α = 0, based on Equation (37), there exists a very small constant β ∈ (0, − ϵ) which, due to the continuity of (37), makes the following hold…”

Section: Synchronisation Analysis Based On Prsmentioning

confidence: 99%

“…Two methods for solving the impulse saturation problem are proposed: the dead-zone nonlinear method and the polyhedral representation. Besides, many scholars have also applied impulse saturation control theory to multi-intelligent body systems, complex networks, and other related fields [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

The Synchronisation Problem of Chaotic Neural Networks Based on Saturation Impulsive Control and Intermittent Control

Cao,

Li,

Leung

2024

Mathematics

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“…Specifically, the impulsive noises generated in real world, such as the sound of the door suddenly shutting, is a major challenge to be solved in training algorithms. To overcome the challenge, some robust adaptive algorithms have been developed, such as adaptive learning-rate saturation and sign algorithms [19], [20], but these algorithms cannot effectively train the nonlinear model. To suppress the impulsive noises in nonlinear model, various detection techniques of the impulsive noises using the neural network and other nonlinear filters have been introduced [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning-Rate Backpropagation Neural Network Algorithm Based on the Minimization of Mean-Square Deviation for Impulsive Noises

Kim

Lee

et al. 2020

IEEE Access

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This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and robustness to impulsive noises. The learning rates of the weights in each hidden layer are derived to minimize the upper bound of the MSD obtained by the analysis, which guarantees a fast convergence rate in a stable range. Moreover, by adopting the variance of the kind of the measurement noises in each layer through the variance of the error signals, the proposed scheme provides robustness to the impulsive noises. The performance of the proposed algorithm is evaluated on various sequential signals and industrial data including the impulsive noise and compared with conventional ALR-BPNN algorithms. Simulation results indicate that the proposed algorithm outperforms the existing algorithms. INDEX TERMS Adaptive learning rate, neural network, mean-square deviation, impulsive noises.

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