郑州大学学报（工学版）2024，Vol.45Issue(4)：38-45,8.DOI:10.13705/j.issn.1671-6833.2024.04.005
基于去趋势多重互相关的深度回声状态网络剪枝算法
Deep Echo State Network Pruning Algorithm Based on Detrended Multiple Cross-correlation
摘要
Abstract
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.关键词
深度回声状态网络/结构优化/剪枝/去趋势多重互相关/时间序列预测Key words
deep echo state network/structure optimization/pruning/detrended multiple cross-correlation/time series prediction分类
信息技术与安全科学引用本文复制引用
孙晓川,王宇,李莹琦,黄天宇..基于去趋势多重互相关的深度回声状态网络剪枝算法[J].郑州大学学报（工学版）,2024,45(4):38-45,8.基金项目
河北省海洋生态修复与智慧海洋工程研究中心开放基金项目(HBMESO2315) （HBMESO2315）
