Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding window algorithm combined with entropy is applied t...

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Bibliographic Details
Main Authors: Jiyan Liu, Yong Zhang, Yuyang Zhou, Jing Chen
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Mathematics
Subjects:
stochastic processes
non-stationary and variable conditions
event-triggered conditions
sliding window algorithm
information entropy
Online Access:https://www.mdpi.com/2227-7390/12/5/667

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