Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions
Jiyan Liu,
Yong Zhang,
Yuyang Zhou
et al.
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 s… Show more
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