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

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Abstract

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.

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Correspondence to Young Hoon Joo.

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This work was jointly supported by the Basic Science Research program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1A6A1A03013567) and by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20174030201670). This work was supported in part by the CSIR. 25(0274)/17/EMR-II dated 27/04/2017.

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Syed Ali, M., Gunasekaran, N. & Joo, Y.H. Sampled-Data State Estimation of Neutral Type Neural Networks with Mixed Time-Varying Delays. Neural Process Lett 50, 357–378 (2019). https://doi.org/10.1007/s11063-018-9946-x

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