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

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

연구 분야: Artificial Intelligence

학회: International Journal of Information Technology

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.