  Industrial particle accelerators inherently operate in much dirtier
environments than typical research accelerators. This leads to an increase in
noise both in the RF system and in other electronic systems. Combined with the
fact that industrial accelerators are mass produced, there is less attention
given to optimizing the performance of an individual system. As a result,
industrial systems tend to under perform considering their hardware hardware
capabilities. With the growing demand for accelerators for medical
sterilization, food irradiation, cancer treatment, and imaging, improving the
signal processing of these machines will increase the margin for the deployment
of these systems. Our work is focusing on using machine learning techniques to
reduce the noise of RF signals used for pulse-to-pulse feedback in industrial
accelerators. We will review our algorithms, simulation results, and results
working with measured data. We will then discuss next steps for deployment and
testing on an industrial system.
