| Autor: |
Wang CR, Long F, Xie KY, Wang HT, Zhang CK, He Y |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on cybernetics [IEEE Trans Cybern] 2024 Jul; Vol. 54 (7), pp. 4164-4176. Date of Electronic Publication: 2024 Jul 11. |
| DOI: |
10.1109/TCYB.2024.3365709 |
| Abstrakt: |
In this article, several improved stability criteria for time-varying delayed neural networks (DNNs) are proposed. A degree-dependent polynomial-based reciprocally convex matrix inequality (RCMI) is proposed for obtaining less conservative stability criteria. Unlike previous RCMIs, the matrix inequality in this article produces a polynomial of any degree in the time-varying delay, which helps to reduce conservatism. In addition, to reduce the computational complexity caused by dealing with the negative definite of the high-degree terms, an improved lemma is presented. Applying the above matrix inequalities and improved negative definiteness condition helps to generate a more relaxed stability criterion for analyzing time-varying DNNs. Two examples are provided to illustrate this statement. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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