Learning or developing dynamic models from experimental data is crucial in monitoring and control of processes. In general, measurements of both inputs and outputs of a process are subject to noise. Model development …
Rapid process development using data generated in laboratory is important in chemical, pharma, specialty and biotech industries. Furthermore, growth in product demand has pushed these industries to adopt continuous …
Learning or developing dynamic models from experimental data is crucial in monitoring and control of processes. In general, measurements of both inputs and outputs of a processare subject to noise. Model development from …
