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Machine learning for industrial sensing and control: A survey and practical perspective
Soft sensing contains a wealth of industrial applications of statistical and machine learning methods.
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.
On-line Bayesian parameter estimation in general non-linear state-space models: A tutorial and new results
The simultaneous estimation is performed by filtering an extended vector of states and parameters using an adaptive sequential-importance-resampling (SIR) filter with a kernel density estimation method.
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