关键词: 回声状态网络, 混沌时间序列, 储备池计算, 稳定性, 长期预测

Abstract: We proposed an improved echo state neural network model for the analysis and prediction of long-term behavior of complex systems. The model introduced the delayed feedback of hidden layer state to reflect the influence of the past time information on the current state of the system,  avoiding the shortcomings of weak memory ability and difficulty of obtaining optimal parameters in traditional echo state network methods.

Key words: echo state network, chaotic time series, reservoir computing, stability, long-term prediction