On global asymptotic stability of fully connected recurrent neural networks
نویسندگان
چکیده
Conditions for Global Asymptotic Stability (GAS) of a nonlinear relaxation process realized by a Recurrent Neural Network (RNN) are provided. Existence. convergence, and robustness of such a process are analyzed. This is undertaken based upon the Contraction Mapping Theorein (CMT) and the corresponding Fixed Point Iteration (FPI). Upper bounds for such a process are shown to be the conditions of convergence for a commonly analyzed RNN with a linear state dependence.
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