Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

Top Cited Papers

2 April 2004

journal article
other
Published by American Association for the Advancement of Science (AAAS) in Science

Vol. 304 (5667) , 78-80
https://doi.org/10.1126/science.1091277

Abstract

We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.

Keywords

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