New event based H∞ state estimation for discrete-time recurrent delayed semi-markov jump neural networks via a novel summation inequality

• Autorzy: Cao Yang , Maheswari K. , Dharan S. , Sivaranjani K.

Identyfikatory

DOI

10.2478/jaiscr-2022-0014

• Języki publikacji: EN

Abstrakty

EN

This paper investigates the event-based state estimation for discrete-time recurrent delayed semi-Markovian neural networks. An event-triggering protocol is introduced to find measurement output with a specific triggering condition so as to lower the burden of the data communication. A novel summation inequality is established for the existence of asymptotic stability of the estimation error system. The problem addressed here is to construct an H∞ state estimation that guarantees the asymptotic stability with the novel summation inequality, characterized by event-triggered transmission. By the Lyapunov functional technique, the explicit expressions for the gain are established. Finally, two examples are exploited numerically to illustrate the usefulness of the new methodology.

Słowa kluczowe

EN

discrete-time neural networks; mixed time delays; asymptotic stability; eventtriggered control

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2022
• Tom: Vol. 12, No. 3
• Strony: 207--227
• Opis fizyczny: Bibliogr. 37 poz., rys.

Twórcy

autor

Cao Yang

• School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China

autor

Maheswari K.

• Department of Mathematics, Kumaraguru College of Technology, Coimbatore, Tamilnadu, India-641 049

autor

Dharan S.

• Department of Mathematics, Kumaraguru College of Technology, Coimbatore, Tamilnadu, India-641 049

autor

Sivaranjani K.

• Department of Mathematics, Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India-641 114

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