水准点(测量)
混乱的
油藏计算
计算机科学
回声状态网络
非线性系统
人工智能
任务(项目管理)
循环神经网络
人工神经网络
Echo(通信协议)
无线
信号(编程语言)
频道(广播)
机器学习
算法
电信
工程类
物理
量子力学
程序设计语言
系统工程
地理
计算机网络
大地测量学
作者
Herbert Jaeger,Harald Haas
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2004-04-02
卷期号:304 (5667): 78-80
被引量:2894
标识
DOI:10.1126/science.1091277
摘要
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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