Abstract
This paper studies the sampled-data state-estimation problem of delayed complex-valued neural networks (CVNNs). By using Lyapunov–Krasovskii functional (LKF), standard integral inequality together with the reciprocal convex approach, a delay-dependent condition is established in terms of the solution to linear matrix inequalities (LMIs) such that the consider CVNNs is asymptotically stable. As a result, an estimator gain matrix can be obtained through sampling instant. Finally, a simulation example is given to illustrate the theoretical analysis.
Original language | English |
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Journal | International Journal of Systems Science |
DOIs | |
Publication status | Accepted/In press - 2019 Jan 1 |
Keywords
- Complex-valued neural networks
- integral inequality
- linear matrix inequality
- Lyapunov method
- sampled-data control
- state-estimation
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications