Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions

Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions

This paper analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and...

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Veröffentlicht in:ISA transactions 2024-01, Vol.144, p.145-152
Hauptverfasser: Lan, Jinbao, Zhang, Xian, Wang, Xin
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description This paper analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and globally robustly exponentially stable equilibrium point. This method not only avoids the difficult to set up any Lyapunov-Krasovskii functional, but also derives simpler global robust exponential stability criteria. Compared with the data from other literature, the robust exponential stability criteria obtained in this paper have been presented to have more merits theoretically and numerically.
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title Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions
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