Robust Adaptive Autonomous Systems
Dr. Hairong Gu will present his PhD research topic on Robust Adaptive Autonomous Systems (RAAS) for Optimization.
Many complex real-world experimental scenarios, particularly in quest of prediction accuracy, often encounter difficulties to conduct experiments using an existing experimental procedure for the following two reasons. First, the existing experimental procedures require a parametric model to serve as the proxy of the latent data structure or data-generating mechanism at the beginning of an experiment. However, for those experimental scenarios of concern, a sound model is often unavailable before an experiment. Second, those experimental scenarios usually contain a large number of design variables, which potentially leads to a lengthy and costly data collection cycle.
Addressing the challenges, the present study developed a new experimental procedure named as robust adaptive autonomous system (RAAS) for sequential experiments which performs function estimation, variable selection, reverse prediction and design optimization on the fly during the the process of an experiment.
A case study was presented that applied this approach to producing carbon nanotube.