<abstract xmlns="http://www.w3.org/1999/xhtml">

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 <italic>H</italic>∞ 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.
</abstract>

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.

New Event Based <italic>H</italic>∞ State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

New Event Based  State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

This paper investigates the event-based state estimation for discrete-time recurrent delayed semi-Markovian neural networks. An event-triggering protocol is...

New Event Based H_∞ State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

Online veröffentlicht: 23. Juli 2022

Seitenbereich: 207 - 221

Eingereicht: 02. Feb. 2022

Akzeptiert: 30. Juni 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0014

Schlüsselwörter
Discrete-time neural networks, Mixed time delays, asymptotic stability, event-triggered control

© 2022 Yang Cao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

New Event Based H∞ State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

Online veröffentlicht: 23. Juli 2022

Seitenbereich: 207 - 221

Eingereicht: 02. Feb. 2022

Akzeptiert: 30. Juni 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0014

SchlüsselwörterDiscrete-time neural networks, Mixed time delays, asymptotic stability, event-triggered control

© 2022 Yang Cao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

New Event Based H_∞ State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

Schlüsselwörter
Discrete-time neural networks, Mixed time delays, asymptotic stability, event-triggered control