Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.
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.
Jaeger
Herbert
H
International University Bremen, Bremen D-28759, Germany. h.jaeger@iu-bremen.de
Haas
Harald
H