Abstract
This paper investigates the global exponential stability for a stochastic bidirectional associative memory (BAM) neural network with time-varying delays. Based on the principle of graph theory, a new method for pth moment exponential stability is derived by combining some inequalities, Lyapunov method and stochastic analysis. The obtained criteria have close relations to the topology property of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the theoretical results.
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Acknowledgments
The authors really appreciate the reviewers’ valuable comments. This work was supported by the NNSF of China (Nos. 11301115, 11301112, 11271101 and 51208150) and the NSF of Shandong Province (No. ZR2013AQ003).
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Su, H., Zhao, Y., Li, W. et al. A graph-theoretic approach to exponential stability of stochastic BAM neural networks with time-varying delays. Neural Comput & Applic 27, 2055–2063 (2016). https://doi.org/10.1007/s00521-015-2005-x
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DOI: https://doi.org/10.1007/s00521-015-2005-x
Keywords
- Stochastic BAM neural network
- Time-varying delays
- Exponential stability
- Lyapunov–Krasovskii functional
- Graph theory