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

TLDR

ESNs employ artificial recurrent neural networks, a learning mechanism also suggested in biological brains. The study introduces a method for learning nonlinear systems using echo state networks. The method uses echo state networks, a form of artificial recurrent neural network. The method is computationally efficient, improves chaotic time series prediction accuracy by 2400×, and reduces communication channel error rates by two orders of magnitude.

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.