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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Pages (from-to) | 303-312 |
Number of pages | 10 |
Journal | International Journal of Systems Science |
Volume | 51 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2020 Jan 25 |
Keywords
- Complex-valued neural networks
- Lyapunov method
- integral inequality
- linear matrix inequality
- sampled-data control
- state-estimation
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications