Exponential Stability of Neural Networks with Distributed Time Delays and Strongly Nonlinear Activation Functions

In this paper, we provided a new technique based on the concept of comparison. Different from the Lyapunov method, the new technique showed that if the given conditions hold then the any state of neural networks with distributed time delays and strongly nonlinear activation functions is always bounded by exponential convergence function. In addition, some sufficient conditions are obtained to guarantee that such neural network is globally exponentially stable, or locally exponentially stable. Furthermore, we obtained the estimates of the exponential convergence rates and the region of exponential convergence.

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