This article proposes a first theoretical performance analysis of the training phase of large dimensional linear echo-state networks. This analysis is based on advanced methods of random matrix theory. The results provide some new insights on the core features of such networks, thereby helping the practitioner when using them.
['Romain Couillet', 'Gilles Wainrib', 'Harry Sevi', 'Hafiz Tiomoko Ali']
Training performance of echo state neural networks