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      Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.

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          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.

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          Author and article information

          Journal
          Science
          Science (New York, N.Y.)
          American Association for the Advancement of Science (AAAS)
          1095-9203
          0036-8075
          Apr 02 2004
          : 304
          : 5667
          Affiliations
          [1 ] International University Bremen, Bremen D-28759, Germany. h.jaeger@iu-bremen.de
          Article
          304/5667/78
          10.1126/science.1091277
          15064413
          aac98428-35b7-4937-9c8b-d9eeecb2404c
          History

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