Fractional Order Echo State Network for Time Series Prediction

被引：13

作者：

Yao, Xianshuang ^{[1
]}

Wang, Zhanshan ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2020年 / 52卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Fractional order; Echo state network; Time series prediction; Parameter optimization; OPTIMIZATION; SYSTEMS;

D O I：

10.1007/s11063-020-10267-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this brief, considering the infinite memory of fractional-order differential equation, a fractional-order echo state network (FESN) is given for time series prediction. For the FESN, the reservoir state differential equation is replaced with fractional-order differential equation. According to the advantages of FESN, the dynamic characteristics of a class of time series can be fully reflected. In order to improve the prediction performance of FESN, a fractional-order output weights learning method and a fractional-order parameter optimization method are given to train the output weights and optimize the reservoir parameters, respectively. Finally, two numerical examples are used to show the effectiveness of FESN.

引用

页码：603 / 614

页数：12

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