Echo State Network (ESN) is often used for time series prediction and chaotic series prediction.But in the training process of ESN model, the reserve pool will involve a large number of calculations, so the reserve pool may have node redundancy, resulting in inaccurate training model. It is very important to select the active nodes in the reserve pool because the predicted nodes are very few compared with all the nodes in the reserve pool. Based on the characteristics of the original vector, compressed sensing can reduce the dimension of the vector so as to obtain the activated nodes. In this paper, the ESN model is added with compressed sensing, which simplifies the reserve pool part of the ESN model. The simulation results show that our method can not only reduce the calculation amount of training effectively, but also improve the accuracy.

回声状态网络（ESN）通常用于时间序列预测和混沌序列预测。但是在ESN模型的训练过程中，备用池将涉及大量的计算，因此备用池可能具有节点冗余性，从而导致训练模型不准确。选择备用池中的活动节点非常重要，因为与备用池中的所有节点相比，预测的节点很少。基于原始矢量的特征，压缩感知可以减小矢量的维数，从而获得激活的节点。在本文中，ESN模型添加了压缩感知，从而简化了ESN模型的备用池部分。仿真结果表明，该方法不仅可以有效地减少训练的计算量，而且可以提高训练的准确性。