Error entropy criterion in echo state network training.
Levy Boccato, Daniel G. Silva, Denis G. Fantinato, Kenji Nose-Filho, Rafael Ferrari, Romis Attux, Aline O. Neves, Jugurta Montalvão, João Marcos Travassos Romano · 2013
Abstract. Echo state networks offer a promising possibility for an effective use of recurrent structures as the presence of feedback is accompanied with a relatively simple training process. However, such simplicity, which is obtained through the use of an adaptive linear readout that minimizes the mean-squared error, limits the capability of exploring the statistical information of the involved signals. In this work, we apply an informationtheoretic learning framework, based on the error entropy criterion, to the ESN training, in order to improve the performance of the neural model, whose advantages are analyzed in the context of supervised channel equalization problem. 1