Deep Echo State Network (DeepESN) is an extension of the ESN model towards the deep learning paradigm (Deep Reservoir Computing). A DeepESN is a deep Recurrent Neural Network composed by a hierarchy of recurrent layers intrinsically able to develop hierarchical and distributed temporal features. Such characteristics make DeepESN suitable for time-series and sequences processing.
All details about DeepESN model are described in the reference paper (CITATION REQUEST):
C. Gallicchio, A. Micheli, L. Pedrelli,
"Deep Reservoir Computing: A Critical Experimental Analysis", Neurocomputing, 2017, vol. 268, pp. 87-99
The design of DeepESN model in multivariate time-series prediction tasks is described in the following paper:
C. Gallicchio, A. Micheli, L. Pedrelli,
"Design of deep echo state networks", Neural Networks, 2018, vol. 108, pp. 33-47
AUTHOR INFORMATION
Luca Pedrelli
luca.pedrelli@di.unipi.it
lucapedrelli@gmail.com
Department of Computer Science - University of Pisa (Italy)
Computational Intelligence & Machine Learning (CIML) Group
http://www.di.unipi.it/groups/ciml/