Yuan-Yuan Wu, Tao Li and Yu-Qiang Wu. Improved Exponential Stability Criteria for Recurrent Neural Networks with Time-varying Discrete and Distributed Delays. International Journal of Automation and Computing, vol. 7, no. 2, pp. 199-204, 2010. DOI: 10.1007/s11633-010-0199-z
Citation: Yuan-Yuan Wu, Tao Li and Yu-Qiang Wu. Improved Exponential Stability Criteria for Recurrent Neural Networks with Time-varying Discrete and Distributed Delays. International Journal of Automation and Computing, vol. 7, no. 2, pp. 199-204, 2010. DOI: 10.1007/s11633-010-0199-z

Improved Exponential Stability Criteria for Recurrent Neural Networks with Time-varying Discrete and Distributed Delays

  • In this paper, the problem of the global exponential stability analysis is investigated for a class of recurrent neural networks (RNNs) with time-varying discrete and distributed delays. Due to a novel technique when estimating the upper bound of the derivative of Lyapunov functional, we establish new exponential stability criteria in terms of LMIs. It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to show the effectiveness of the proposed results.
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