Protocol-based fault detection for discrete-time memristive neural networks with quantization effect

Cheng, Jun; Lin, An; Cao, Jinde; Qiu, Jianlong; Qi, Wenhai

doi:10.1016/j.ins.2022.10.018

Cited by 10 publications

(4 citation statements)

References 46 publications

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“…Theorem 1. Under Assumptions 1-2 and mixed impulsive feedback controller (13), the global TFMPS between memristive neural networks ( 3) and ( 8) can be achieved, if there exist suitable…”

Section: Synchronization Analysis Resultsmentioning

confidence: 99%

“…δ(•) represents the Dirac delta function. Combining error system (12) and mixed impulsive controller (13), one can obtain…”

Section: Theoretical Foundation and Model Establishmentmentioning

confidence: 99%

“…The research results have shown that using memristors to replace neuronal protrusions can establish various memristive neural networks with parallel computing capabilities [11]. Therefore, memristive neural networks (MNNs), as a special type of state-dependent network model, naturally receive much attention from scientific research personnel [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

Fan,

Rao,

Shi

et al. 2024

Fractal Fract

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This paper shows solicitude for the generalized projective synchronization of Caputo fractional-order uncertain memristive neural networks (FOUMNNs) with multiple delays. By extending the constant scale factor to the time-varying function matrix, we establish an extraordinary synchronization mode called time-varying function matrix projection synchronization (TFMPS), which is a generalized version of traditional matrix projection synchronization, modified projection synchronization, complete synchronization, and anti-synchronization. To achieve the goal of TFMPS, we design a novel mixed controller including the open loop feedback control and impulsive control, which employs the state information in the time-delayed interval and the sampling information at the impulse instants. It has a prominent advantage that impulse intervals are not restricted by time delays. To establish the connection between the error system and the auxiliary system, a generalized fractional-order comparison theorem with time-varying coefficients and impulses is established. Applying the stability theory, the comparison theorem, and the Laplace transform, new synchronization criteria of FOUMNNs are acquired under the mixed impulsive control schemes, and the derived synchronization theorem and corollary can effectively expand the correlative synchronization achievements of fractional-order systems.

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“…Theorem 1. Under Assumptions 1-2 and mixed impulsive feedback controller (13), the global TFMPS between memristive neural networks ( 3) and ( 8) can be achieved, if there exist suitable…”

Section: Synchronization Analysis Resultsmentioning

confidence: 99%

“…δ(•) represents the Dirac delta function. Combining error system (12) and mixed impulsive controller (13), one can obtain…”

Section: Theoretical Foundation and Model Establishmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

Fan,

Rao,

Shi

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…Memristive neural networks (MNNs) are a class of state-dependent switching models that have been widely applied in various fields, including machine learning, stability analysis, image encryption, and fault detection [1][2][3][4][5][6]. Due to the memory characteristics of memristors, which are similar to the behavior of neuronal synapses, MNNs can model the working mechanisms of neurons in the human brain effectively.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

et al. 2023

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This article addresses the issue of drive–response synchronization in fractional-order multi-link memristive neural networks (FMMNN) with multiple delays, under hybrid impulsive feedback control. To address the impact of multiple delays on system synchronization, an extended fractional-order delayed comparison principle incorporating impulses is established. By leveraging Laplace transform, Mittag–Leffler functions, the generalized comparison principle, and hybrid impulsive feedback control schemes, several new sufficient conditions are derived to ensure synchronization in the addressed FMMNN. Unlike existing studies on fractional-order single-link memristor-based systems, our response network is a multi-link model that considers impulsive effects. Notably, the impulsive gains αi are not limited to a small interval, thus expanding the application range of our approach (αi∈(−2,0)∪(−∞,−2)∪(0,+∞)). This feature allows one to choose impulsive gains and corresponding impulsive intervals that are appropriate for the system environment and control requirements. The theoretical results obtained in this study contribute to expanding the relevant theoretical achievements of fractional-order neural networks incorporating memristive characteristics.

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Multi-subspace self-attention siamese networks for fault diagnosis with limited data

Zhang,

Chen,

et al. 2023

SIViP

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Protocol-based fault detection for discrete-time memristive neural networks with quantization effect

Cited by 10 publications

References 46 publications

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

Multi-subspace self-attention siamese networks for fault diagnosis with limited data

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