Broad Learning for Optimal Short-Term Traffic Flow Prediction

被引：4

作者：

Liu, Di ^{[1
]}

Yu, Wenwu ^{[1
,2
]}

Baldi, Simone ^{[2
,3
]}

机构：

[1] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China

[2] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China

[3] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CD Delft, Netherlands

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT I | 2019年 / 11554卷

基金：

中国国家自然科学基金;

关键词：

Broad Learning System; Traffic flow prediction; Flat network; Fast least-square methods; DEEP; ARCHITECTURE; NETWORKS; SYSTEM; MODEL;

D O I：

10.1007/978-3-030-22796-8_25

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 work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy.

引用

页码：232 / 239

页数：8

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