Szczegóły publikacji
Opis bibliograficzny
Circuit implementation and quasi-stabilization of delayed inertial memristor-based neural networks / Youming Xin, Zunshui Cheng, Jinde Cao, Leszek RUTKOWSKI, Yaning Wang // IEEE Transactions on Neural Networks and Learning Systems ; ISSN 2162-237X. — 2024 — vol. 35 no. 1, s. 1394–1400. — Bibliogr. s. 1399-1400, Abstr. — Publikacja dostępna online od: 2022-05-16. — L. Rutkowski - dod. afiliacja: Systems Research Institute of the Polish Academy of Sciences, Warsaw
Autorzy (5)
- Xin Youming
- Cheng Zunshui
- Cao Jinde
- AGHRutkowski Leszek
- Wang Yaning
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151275 |
|---|---|
| Data dodania do BaDAP | 2024-02-20 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TNNLS.2022.3173620 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Transactions on Neural Networks and Learning Systems |
Abstract
In this brief, we consider the stability of inertial memristor-based neural networks with time-varying delays. First, delayed inertial memristor-based neural networks are modeled as continuous systems in the flux-current-voltage-time domain via the mathematical model of Hewlett-Packard (HP) memristor. Then, they are reduced to delayed inertial neural networks with interval parameters uncertainties. Quasi-equilibrium points and quasi-stability are proposed. Quasi-stability criteria of delayed inertial memristor-based neural networks are obtained by matrix measure method, the Halanay inequality, and uncertainty technologies. In the end, a numerical example is provided to show the validity of our results. IEEE
