Pages that link to "Item:Q2077652"
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The following pages link to Echo state networks trained by Tikhonov least squares are \(L^2(\mu)\) approximators of ergodic dynamical systems (Q2077652):
Displaying 15 items.
- Embedding and approximation theorems for echo state networks (Q1982435) (← links)
- Dimension reduction in recurrent networks by canonicalization (Q2076953) (← links)
- Echo state networks are universal (Q2182904) (← links)
- A local echo state property through the largest Lyapunov exponent (Q2418123) (← links)
- The asymptotic performance of linear echo state neural networks (Q2834516) (← links)
- Symmetry kills the square in a multifunctional reservoir computer (Q5011745) (← links)
- Reservoir Computing with an Inertial Form (Q5158627) (← links)
- (Q5259907) (← links)
- Predicting turbulent dynamics with the convolutional autoencoder echo state network (Q6067855) (← links)
- Fading memory echo state networks are universal (Q6078702) (← links)
- Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers (Q6118141) (← links)
- Learning Theory for Dynamical Systems (Q6132792) (← links)
- Learning strange attractors with reservoir systems (Q6169729) (← links)
- Data-informed reservoir computing for efficient time-series prediction (Q6549981) (← links)
- Data-driven cold starting of good reservoirs (Q6629745) (← links)
