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- W972871723 abstract "It is well known that activation functions of RNNs are important characters of RNNs. Usually, activation functions are nonlinear functions, thus they make RNNs be described by nonlinear systems. Since they represent some structural parts of the the nonlinear systems, they crucially decide the dynamical properties of RNNs.KeywordsPeriodic SolutionEquilibrium PointActivation FunctionConvergence AnalysisRecurrent Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W972871723 date "2004-01-01" @default.
- W972871723 modified "2023-09-27" @default.
- W972871723 title "Delayed Recurrent Neural Networks with Global Lipschitz Activation Functions" @default.
- W972871723 doi "https://doi.org/10.1007/978-1-4757-3819-3_6" @default.
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