Sampled-data state-estimation of delayed complex-valued neural networks

Nallappan Gunasekaran, Guisheng Zhai

Research output: Contribution to journalArticle

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 languageEnglish
JournalInternational Journal of Systems Science
DOIs
Publication statusAccepted/In press - 2019 Jan 1

Fingerprint

State estimation
Neural networks
Linear matrix inequalities
Sampling

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 journalArticle

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