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<p>In order to regulate neural networks with leakage delays, a novel sampled-data control approach and its stability analysis are investigated in this research. We used sampled-data control approach to bring the unstable systems under control. This research uses sampled data to examine the regulation of a neural network with additive time-varying delays and leaking delays. A unique criterion based on linear matrix inequality (LMI) is derived to ensure the asymptotic stability of neural networks. To determine the gain matrix for the planned sampled-data controllers, these generated LMIs are applied. The results are obtained by formulating a novel Lyapunov functional. In addition, no free-weighting matrices or convex combination techniques are used. Finally, to demonstrate the efficiency of our theoretical findings, a numerical example and accompanying computational models have been provided. Ultimately, numerical examples illustrate the efficacy and prudence of the theoretical findings.</p>

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Control of sampled data for neural networks with time-varying delays and leakage delays

  • 期刊论文

  • 发表日期：

    2025年4月15日

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  • S. Ravi Chandra,
  • P. Rajakumari,
  • G. Shanthi,
  • P. Baskar,
  • V. Umesha,
  • S. Padmanabhan

摘要

In order to regulate neural networks with leakage delays, a novel sampled-data control approach and its stability analysis are investigated in this research. We used sampled-data control approach to bring the unstable systems under control. This research uses sampled data to examine the regulation of a neural network with additive time-varying delays and leaking delays. A unique criterion based on linear matrix inequality (LMI) is derived to ensure the asymptotic stability of neural networks. To determine the gain matrix for the planned sampled-data controllers, these generated LMIs are applied. The results are obtained by formulating a novel Lyapunov functional. In addition, no free-weighting matrices or convex combination techniques are used. Finally, to demonstrate the efficiency of our theoretical findings, a numerical example and accompanying computational models have been provided. Ultimately, numerical examples illustrate the efficacy and prudence of the theoretical findings.