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Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays
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文摘

Different from the prior works, here proportional delays are unbounded and time-varying.

Periodicity existing results of delay neural networks cannot be applied to the system in the paper.

The advantage is that the network's running time can be controlled by the network allowed delays.

Delay differential inequality is established, which is not (generalized) Halanay inequality.

The nonlinear activation functions are not necessarily differentiable, bounded, monotonic.

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