Global Output Convergence of RNN in Menger Probabilistic Metric
نویسندگان
چکیده
This paper discusses the global output convergence for continuous time recurrent neural networks with continuous decreasing as well as increasing activation functions in probabilistic metric space. We establish three sufficient conditions to guarantee the global output convergence of this class of neural networks. The present result does not require symmetry in the connection weight matrix. The convergence result is useful in the design of recurrent neural networks with different converging conditions.
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