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Embedding and approximation theorems for echo state networks
Echo State Networks (ESNs) are a class of single-layer recurrent neural networks that have enjoyed recent attention. In this paper we prove that a suitable ESN, trained on a series of measurements of an invertible dynamical...
Delay embedding Dynamical system Lorenz equations Persistent homology recurrent neural networks reservoir computing /dk/atira/pure/subjectarea/asjc/2800/2805 name=Cognitive Neuroscience /dk/atira/pure/subjectarea/asjc/1700/1702 name=Artificial Intelligence /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics
Embedding and approximation theorems for echo state networks
Echo State Networks (ESNs) are a class of single-layer recurrent neural networks that have enjoyed recent attention. In this paper we prove that a suitable ESN, trained on a series of measurements of an invertible dynamical...
Delay embedding Dynamical system Lorenz equations Persistent homology recurrent neural networks reservoir computing /dk/atira/pure/subjectarea/asjc/2800/2805 name=Cognitive Neuroscience /dk/atira/pure/subjectarea/asjc/1700/1702 name=Artificial Intelligence /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics