Abstract
Machine learning (ML) has become a valuable tool in particle accelerator control, with growing potential for beam parameter correction. In this study, we present preliminary ML applications at HLS-II, using Lasso regression for online tune correction and a neural network (NN) for beta function simulation correction. Both models were trained with supervised learning on measured beam parameter data, while an improved genetic algorithm optimized the NN structure. Our results show that the ML-based approach achieves competitive correction quality with fewer steps, making it a promising method for future particle accelerator applications and other fields.
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