Echo state networks: appeal and challenges

The echo state network (ESN) has recently been proposed for modeling complex dynamic systems. The ESN is a sparsely connected recurrent neural network with most of its weights fixed a priori to randomly chosen values. The only trainable weights are those on links connected to the outputs. The ESN ca...

Full description

Saved in:
Bibliographic Details
Published inProceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 Vol. 3; pp. 1463 - 1466 vol. 3
Main Author Prokhorov, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Backpropagation
Eigenvalues and eigenfunctions
Electronic mail
Histograms
Least squares approximation
Least squares methods
Output feedback
Recurrent neural networks
Reservoirs
Roads
Online AccessGet full text

Cover

More Information
Summary:The echo state network (ESN) has recently been proposed for modeling complex dynamic systems. The ESN is a sparsely connected recurrent neural network with most of its weights fixed a priori to randomly chosen values. The only trainable weights are those on links connected to the outputs. The ESN can demonstrate remarkable performance after seemingly effortless training. This brief paper discusses ESN in a broader context of applications of recurrent neural networks (RNN) and highlights challenges on the road to practical applications.
ISBN:0780390482
9780780390485
ISSN:2161-4393
DOI:10.1109/IJCNN.2005.1556091