A Degree-Dependent Polynomial-Based Reciprocally Convex Matrix Inequality and Its Application to Stability Analysis of Delayed Neural Networks

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 inequa...

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Veröffentlicht in:	IEEE transactions on cybernetics 2024-07, Vol.54 (7), p.4164-4176
Hauptverfasser:	Wang, Chen-Rui, Long, Fei, Xie, Ke-You, Wang, Hui-Ting, Zhang, Chuan-Ke, He, Yong
Format:	Artikel
Sprache:	eng
Schlagworte:	Delays High-order polynomials Linear matrix inequalities Neural networks Polynomials reciprocally convex matrix inequality (RCMI) stability Stability analysis Stability criteria Symmetric matrices Thermal stability time-varying delay
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