The''echo state''approach to analysing and training recurrent neural networks
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[1] Barak A. Pearlmutter. Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.
[2] D. Broomhead,et al. Takens embedding theorems for forced and stochastic systems , 1997 .
[3] Snehasis Mukhopadhyay,et al. Adaptive control using neural networks and approximate models , 1997, IEEE Trans. Neural Networks.
[4] Masahiro Kimura,et al. Learning dynamical systems by recurrent neural networks from orbits , 1998, Neural Networks.
[5] James McNames,et al. Innovations in local modeling for time series prediction , 1999 .
[6] B. Farhang-Boroujeny,et al. Adaptive Filters: Theory and Applications , 1999 .
[7] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[8] L. Abbott,et al. Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.
[9] Local Modeling Optimization for Time Series Prediction , 2000 .
[10] Amir F. Atiya,et al. New results on recurrent network training: unifying the algorithms and accelerating convergence , 2000, IEEE Trans. Neural Networks Learn. Syst..
[11] Jürgen Schmidhuber,et al. Applying LSTM to Time Series Predictable through Time-Window Approaches , 2000, ICANN.
[12] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.