Sampled-Data State Estimation of Neutral Type Neural Networks with Mixed Time-Varying Delays

In this paper, we consider the problem of sampled-data control for neutral type neural networks with mixed time-varying delay components. A proper Lyapunov-Krasovskii functional is constructed by dividing the discrete and neutral delay intervals with triple and quadruplex integral terms. By employing the input delay approach, the sampling period is converted into a bounded time-vary delay in the estimation error dynamic. By employing Lyapunov-functional approach and utilizing LMI technique, sufficient conditions have been derived to guarantee that the estimation error dynamics is asymptotically stable. A numerical example is provided to illustrate the usefulness and effectiveness of the obtained results.