基于去趋势多重互相关的深度回声状态网络剪枝算法OA北大核心CSTPCD

To address the problem of undesirable prediction accuracy of a deep echo state network caused by redun-dant structures in the reservoirs,a pruning algorithm for the deep echo state network based on detrended multiple cross-correlation was proposed.Firstly,according to the detrended covariance function and the detrended variance function,the detrended cross-correlation coefficient between each two neurons in the selected reservoirs in turn was calculated,and the detrended cross-correlation matrix was constructed.Based on this matrix,the detrended multi-ple cross-correlation between a selected neuron and all remaining neurons in this reservoir could be evaluated.Sub-sequently,the connections from the highly correlated neurons in each reservoir to the output layer were pruned se-quentially,thus removing redundant components in the network.Finally,the network after pruning was retrained by least squares regression to obtain the optimal deep echo state network topology.Simulation results showed that the prediction accuracy and memory capacity of the deep echo state network optimized by the proposed algorithm on Mackey-Glass time series were improved by 89.80%and 30.93%,respectively,and on Call time series by 14.34%and 0.10%,respectively.