Identification and experiment design for irregularly sampled processes

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Irregular sampling of measured variables is commonplace in the process and biomedical industries. Process variables are measured irregularly either due to unavailability of appropriate sensors or due to more fundamental physical limitations. This project aims to (a) develop modeling algorithms from irregularly sampled data for both linear and nonlinear processes, (b) understand the affect of sampling pattern on the consistency of model estimates, and (c) develop algorithms for experiment design and adaptive estimation.