Szczegóły publikacji
Opis bibliograficzny
Asynchronous fault detection for memristive neural networks with dwell-time-based communication protocol / An Lin, Jun Cheng, Leszek RUTKOWSKI, Shiping Wen, Mengzhuo Luo, Jinde Cao // IEEE Transactions on Neural Networks and Learning Systems ; ISSN 2162-237X. — 2023 — vol. 34 no. 11, s. 9004–9015. — Bibliogr. s. 9013–9014, Abstr. — Publikacja dostępna online od: 2022-03-10. — L. Rutkowski - dod. afiliacja: Systems Research Institute of the Polish Academy of Sciences, Warsaw
Autorzy (6)
- Lin An
- Cheng Jun
- AGHRutkowski Leszek
- Wen Shiping
- Luo Mengzhuo
- Cao Jinde
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150885 |
|---|---|
| Data dodania do BaDAP | 2024-01-16 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TNNLS.2022.3155149 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Transactions on Neural Networks and Learning Systems |
Abstract
This article studies the asynchronous fault detection filter problem for discrete-time memristive neural networks with a stochastic communication protocol (SCP) and denial-of-service attacks. Aiming at alleviating the occurrence of network-induced phenomena, a dwell-time-based SCP is scheduled to coordinate the packet transmission between sensors and filter, whose deterministic switching signal arranges the proper feedback switching information among the homogeneous Markov processes (HMPs) for different scenarios. A variable obeying the Bernoulli distribution is proposed to characterize the randomly occurring denial-of-service attacks, in which the attack rate is uncertain. More specifically, both dwell-time-based SCP and denial-of-service attacks are modeled by means of compensation strategy. In light of the mode mismatches between data transmission and filter, a hidden Markov model (HMM) is adopted to describe the asynchronous fault detection filter. Consequently, sufficient conditions of stochastic stability of memristive neural networks are devised with the assistance of Lyapunov theory. In the end, a numerical example is applied to show the effectiveness of the theoretical method.
