Lagrange Stability for Memristor-Based Neural Networks with Time-Varying Delay via Matrix Measure
In this paper, we study the global exponential stability in Lagrange sense for memristor-based neural networks (MBNNs) with time-varying delays. Based on the nonsmooth analysis and differential inclusion theory, matrix measure technique is employed to establish some succinct criteria which ensure the Lagrange stability of the considered memristive model. In addition, the new proposed criteria are very easy to verify, and they also enrich and improve the earlier publications. Finally, two example are given to demonstrate the validity of the results.
KeywordsMemristor-based Neural Networks Lagrange Stability Matrix Measure
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- 11.Wang, Z., Liu, L., Shan, Q., Zhang, H.: Stability criteria for recurrent neural networks with time-varying delay based on secondary delay partitioning method. IEEE Transactions on Neural Networks and Learning Systems (2015). doi:10.1109/TNNLS.2014.2387434Google Scholar
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