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Global exponential stability of neural networks with globally Lipschitz continuous activations and its application to linear variational inequality problem

2001
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IEEE Transactions on Neural Networks
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This paper investigates the existence, uniqueness, and global exponential stability (GES) of the equilibrium point for a large class of neural networks with globally Lipschitz continuous activations including the widely used sigmoidal activations and the piecewise linear activations. The provided sufficient condition for GES is mild and some conditions easily examined in practice are also presented. The GES of neural networks in the case of locally Lipschitz continuous activations is also

doi:10.1109/72.914529
pmid:18244389
fatcat:gsbsyjftwvcajkyvppawlsv6dm