This paper considers switching stochastic delay neural networks (SSDNNs) with all unstable subsystems. By using discretized Lyapunov-Krasovskii functions (DLKFs) combined with the dwell time method, exponential stability of SSDNNs with all unstable subsystems are analyzed, and several novel stability criteria in mean square are obtained. Comparing with the existing works, our results focus on all unstable subsystems rather than other combinations such as all stable or partially stable subsystems, which is of more research significance. Finally, the correctness of the conclusion is checked by the feasible solutions of two numerical examples.

Stability of stochastic delay switched neural networks with all unstable subsystems: A multiple discretized Lyapunov-Krasovskii functionals method

Karimi H. R.
2022-01-01

Abstract

This paper considers switching stochastic delay neural networks (SSDNNs) with all unstable subsystems. By using discretized Lyapunov-Krasovskii functions (DLKFs) combined with the dwell time method, exponential stability of SSDNNs with all unstable subsystems are analyzed, and several novel stability criteria in mean square are obtained. Comparing with the existing works, our results focus on all unstable subsystems rather than other combinations such as all stable or partially stable subsystems, which is of more research significance. Finally, the correctness of the conclusion is checked by the feasible solutions of two numerical examples.
2022
Discretized Lyapunov-Krasovskii functions
Exponential stability
Stochastic switched neural networks
Time-vary delay
Unstable subsystems
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1205303
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? 22
social impact