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In this paper, we point out that global Lipschitz condition on the activation functions should be revised and the boundedness assumption of the activation functions in the above paper can be removed based on a nonlinear measure approach. Moreover, we present some improved sufficient conditions which are less conservative than those in the above paper in terms of linear matrix inequality (LMI).
The aim of this contribution is to implement a hardware module that performs parametric identification of dynamical systems. The design is based upon the methodology of optimization with Hopfield neural networks, leading to an adapted version of these networks. An outstanding feature of this modified Hopfield network is the existence of weights that vary with time. Since weights can no longer be stored...
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