Robust Stability of Fractional Order Memristive BAM Neural Networks with Mixed and Additive Time Varying Delays

This paper is concerned with the problem of the robust stability of fractional-order memristive bidirectional associative memory (BAM) neural networks. Based on Lyapunov theory, fractional-order differential inequalities and linear matrix inequalities (LMI) are applied to obtain a robust asymptotica...

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Veröffentlicht in:	Fractal and fractional 2022-01, Vol.6 (2), p.62
Hauptverfasser:	Han, Xiuping, Hymavathi, M., Sanober, Sumaya, Dhupia, Bhawna, Syed Ali, M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Associative memory Calculus Equilibrium fractional order Linear matrix inequalities Mathematical analysis memristive BAM neural networks mixed and additive time varying delays Neural networks Recurrent neural networks robust stability Robustness (mathematics) Stability Systems stability
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Beschreibung
Zusammenfassung:	This paper is concerned with the problem of the robust stability of fractional-order memristive bidirectional associative memory (BAM) neural networks. Based on Lyapunov theory, fractional-order differential inequalities and linear matrix inequalities (LMI) are applied to obtain a robust asymptotical stability. Finally, numerical examples are presented.
ISSN:	2504-3110 2504-3110
DOI:	10.3390/fractalfract6020062