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

Jiyan Liu, Yong Zhang (), Yuyang Zhou () and Jing Chen
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Jiyan Liu: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Yong Zhang: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Yuyang Zhou: School of Computing Engineering and Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Jing Chen: School of Science, Jiangnan University, Wuxi 214122, China

Mathematics, 2024, vol. 12, issue 5, 1-20

Abstract: 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 to divide the input and output datasets across different operational conditions, establishing clear data boundaries. Following this, the prediction errors derived from the neural network under different operational states are harnessed to define a set of event-triggered relearning criteria. Once these conditions are triggered, the relevant dataset is used to recalibrate the model to the specific operational condition and predict the data under this operating condition. When the predicted data fall within the training input range of a pre-trained model, we switch to that model for immediate prediction. Compared with the conventional BP neural network model and random vector functional-link network, the proposed model can produce a better estimation accuracy and reduce computation costs. Finally, the effectiveness of our proposed method is validated through numerical simulation tests using nonlinear Hammerstein models with Gaussian noise, reflecting complex stochastic industrial processes.

Keywords: stochastic processes; non-stationary and variable conditions; event-triggered conditions; sliding window algorithm; information entropy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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