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Science 2 April 2004:
Vol. 304. no. 5667, pp. 78 - 80
DOI: 10.1126/science.1091277
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Reports

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

Herbert Jaeger* and Harald Haas

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.

International University Bremen, Bremen D-28759, Germany.

* To whom correspondence should be addressed. E-mail: h.jaeger{at}iu-bremen.de

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Science. ISSN 0036-8075 (print), 1095-9203 (online)