ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。

As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.

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本文主要通过对传统回声状态网络(esn)的结构和学习机理的研究，探讨了回声状态网络对混沌时间序列的预测方法。

In this paper, the traditional echo state network (ESN) through the structure and learning mechanism of the study, on the echo state network prediction method of chaotic time series.

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通过选取具有储备池机制的回声状态网络ESN作为网络预测模型，建立一种直接预测方法，直接构建预测原点和预测时域之间的定量关系。

It selects ESN with the mechanism of the "reservoir" as predict model to set up a direct predict method which directly relates the prediction origin and prediction horizon.

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