Echo State Networks are efficient time-series predictors, which highly depend on the value of the spectral radius of the reservoir connectivity matrix. Based on recent results on the mean field theory of driven random recurrent neural networks, enabling the computation of the largest Lyapunov exponent of an ESN, we develop a cheap algorithm to establish a local and operational version of the Echo State Property.
Cite
@article{arxiv.1402.1619,
title = {A local Echo State Property through the largest Lyapunov exponent},
author = {Mathieu Galtier and Gilles Wainrib},
journal= {arXiv preprint arXiv:1402.1619},
year = {2015}
}