BackgroundEcho-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echo-state approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks.
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机译：背景回声网络（ESN）是一组油藏计算方法的一部分，基本上是递归人工神经网络（ANN）的一种形式。这些方法可以对时间序列数据执行分类任务。回波状态网络的循环ANN具有“回波状态”特性。这种“回声状态”用作褪色记忆：在更远的过去引入网络的样本逐渐消失。 Jaeger H. et al。首先描述了用于循环神经网络训练的回声状态方法。在临床医学中，到目前为止，还没有发表原始研究文章来研究回声状态网络的使用。
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