Quantized $${\mathcal {H}}_\infty$$ stabilization for delayed memristive neural networks

<p>The issue of non-fragile sampled-data control for synchronizing Markov jump Lur'e systems (MJLSs) with time-variant delay was investigated. The time-variant delay allowed for uncertainty that was constrained to an interval with defined upper and lower boundaries. The components of the nonlinear function within the MJLSs were considered to satisfy either Lipschitz continuity or non-decreasing monotonicity. Numerically tractable conditions that ensured stochastic synchronization with a predefined $ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ disturbance attenuation level for the drive-response MJLSs were established, utilizing time-dependent two-sided loop Lyapunov-Krasovskii functionals, together with integral and matrix inequalities. An exact mathematical expression of the desired controller gains can be obtained based on these conditions. Finally, an example with numerical simulation was employed to demonstrate the effectiveness of the proposed control strategies.</p>

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Quantized $${\mathcal {H}}_\infty$$ stabilization for delayed memristive neural networks

Cited by 5 publications

References 43 publications

Secure defense control for memristive recurrent neural networks under denial-of-service attacks with quantized sampled-data signals

Secure defense control for memristive recurrent neural networks under denial-of-service attacks with quantized sampled-data signals

Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts

Non-fragile sampled-data control for synchronizing Markov jump Lur'e systems with time-variant delay

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