Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Algorithms in C++
Recurrent networks: supervised learning
The handbook of brain theory and neural networks
Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
New results on recurrent network training: unifying the algorithms and accelerating convergence
IEEE Transactions on Neural Networks
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Recurrent neural networks are still a challenge in neural investigation. Most commonly used methods have to deal with several problems like local minima, slow convergence or bad learning results because of bifurcations through which the learning system is driven. The following approach, which is inspired by Echo State networks [1], overcomes those problems and enables learning of complex dynamical signals and tasks.