Exponential stability of implicit Euler, discrete-time Hopfield neural networks
نویسنده
چکیده
The exponential stability of continuous-time Hopfield neural networks is not preserved when implemented on digital computers by means of explicit numerical methods, whereas the implicit (or backward) Euler method preserves this exponential stability under exactly the same sufficient conditions as those previously obtained for the continuous model. The proof is based on the nonlinear measure approach, here extended to discrete-time systems. This approach also allows the estimation of the exponential convergence rate of the discrete solutions.
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