Matrix measure based stability criteria for high-order neural networks with proportional delay

Abstract In this paper, the stability is discussed for high-order neural networks with proportional delay. The proportional delay is a time-varying unbounded delay and different from the constant delay, bounded time-varying delays and distributed delays. Based on Lyapunov method, matrix measure and generalized Halanay inequality, a criterion is obtained to ensure the p th exponential stability of high-order neural networks with proportional delay. The result can be extended to the neural networks with proportional delay or multiple proportional delays. The obtained results are simple, effective and easy to be verified. The simulating examples are exploited to illustrate the improvement and advantages of the obtained results in comparison with some existing results.

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