Mixture correntropy with variable center LSTM network for traffic flow forecasting

Mixture correntropy with variable center LSTM network for traffic flow forecasting

Timely and accurate traffic flow prediction is the core of an intelligent transportation system. Canonical long short-term memory (LSTM) networks are guided by the mean square error (MSE) criterion, so it can handle Gaussian noise in traffic flow effectively. The MSE criterion is a global measure of...

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Bibliographic Details
Main Authors: Weiwei Fang, Xiaoke Li, Zhizhe Lin, Jinglin Zhou, Teng Zhou
Format: Article
Language:English
Published: Maximum Academic Press 2024-12-01
Series:Digital Transportation and Safety
Subjects:
traffic flow theory
machine learning
robust modeling
mixture correntropy
Online Access:https://www.maxapress.com/article/doi/10.48130/dts-0024-0023
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https://www.maxapress.com/article/doi/10.48130/dts-0024-0023

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