# ESN Drivers

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Missing docstring for RNN. Check Documenter's build log for details.

ReservoirComputing.MRNNType
MRNN(activation_function, leaky_coefficient, scaling_factor)
MRNN(;activation_function=[tanh, sigmoid], leaky_coefficient=1.0,
scaling_factor=fill(leaky_coefficient, length(activation_function)))

Returns a Multiple RNN initializer, where multiple function are combined in a linear combination with chosen parameters scaling_factor. The activation_function and scaling_factor arguments must vectors of the same size. Multiple combinations are possible, the implementation is based upon a double activation function idea, found in [1].

[1] Lun, Shu-Xian, et al. "A novel model of leaky integrator echo state network for time-series prediction." Neurocomputing 159 (2015): 58-66.

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Missing docstring for GRU. Check Documenter's build log for details.

The GRU driver also provides the user the choice of the possible variant:

ReservoirComputing.FullyGatedType
FullyGated()

Returns a standard Gated Recurrent Unit ESN initializer, as described in [1].

[1] Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).

ReservoirComputing.MinimalType
Minimal()

Returns a minimal GRU ESN initializer as described in [1].

[1] Zhou, Guo-Bing, et al. "Minimal gated unit for recurrent neural networks." International Journal of Automation and Computing 13.3 (2016): 226-234.

Please refer to the original papers for more detail about these architectures.

The states are created using the following function

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Missing docstring for create_states. Check Documenter's build log for details.