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

作者：Herbert Jaeger, Harald Haas · 发表于：Science · 年份：2004 · DOI：10.1126/science.1091277 · 被引用次数：3888 · 研究领域：Neural Networks and Reservoir Computing、Advanced Memory and Neural Computing、Neural Networks and Applications

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.