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 |
|---|---|
| Journal | International Journal of Systems Science |
| DOIs | |
| Publication status | Accepted/In press - 2019 Jan 1 |
Fingerprint
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
Cite this
Sampled-data state-estimation of delayed complex-valued neural networks. / Gunasekaran, Nallappan; Zhai, Guisheng.
In: International Journal of Systems Science, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Sampled-data state-estimation of delayed complex-valued neural networks
AU - Gunasekaran, Nallappan
AU - Zhai, Guisheng
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Complex-valued neural networks
KW - integral inequality
KW - linear matrix inequality
KW - Lyapunov method
KW - sampled-data control
KW - state-estimation
UR - http://www.scopus.com/inward/record.url?scp=85076903437&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076903437&partnerID=8YFLogxK
U2 - 10.1080/00207721.2019.1704095
DO - 10.1080/00207721.2019.1704095
M3 - Article
AN - SCOPUS:85076903437
JO - International Journal of Systems Science
JF - International Journal of Systems Science
SN - 0020-7721
ER -