Abstract
Convergence analysis of recurrent neural networks is an important research direction in the field of neural networks. Novel methods to study the global exponential convergence of recurrent neural networks with variable delays are proposed. A condition for global exponential stability, which is independent of the delays, is derived by the method of delayed inequalities analysis. Another condition for global exponential stability, which depends on the delays, is obtained via the method of constructing a suitable and interesting Lyapunov functional.
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