Delayed Deep Echo State Network and Its Application on Time Series Prediction

被引：0

作者：

Bo Y.-C. ^{[1
]}

Zhang X. ^{[1
]}

Liu B. ^{[1
]}

机构：

[1] College of Control Science and Engineering, China University of Petroleum (East China), Qingdao

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Artificial neural networks; Deep learning; Echo state network; Short-term memory capacity; Time series prediction;

D O I：

10.16383/j.aas.c180264

中图分类号：

学科分类号：

摘要：

To improve the prediction ability of echo state network (ESN) on time series problems, this paper proposes a delayed deep ESN (DDESN) constructing method. In this scheme, multiple sub-reservoirs are connected one by one in sequence, and time delay modules are inserted between every two adjacent sub-reservoirs. The DDESN can transfer a long-term memory task into a series of short-term memory tasks because of the existence of the delay links. It simplifies the solution to long-term dependent task and reduces the difficulty of building a reservoir. Experimental results show that the proposed DDESN has stronger short-term memory capacity, better robustness to randomly initialized parameters, and higher performance on solving time series tasks than a standard ESN. Copyright © 2020 Acta Automatica Sinica. All rights reserved.

引用

页码：1644 / 1653

页数：9

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