Data-Driven Model Reduction, Scientific Frontiers, and Applications ()
- Texas A&M University
- College Station, TX
- Joe C. Richardson Petroleum Engineering Building (RICH) 910
- Ulisses Braga-Neto, Department of Electrical & Computer Engineering
- Partially-Observed Boolean Dynamical Systems
Abstract
We introduced several years ago the partially-observed Boolean dynamical system (POBDS) model as a general framework that includes as special cases various well-known models, such as Boolean Networks with perturbation (BNp) and Probabilistic Boolean Networks (PBN). The optimal minimum mean-square state estimator (MMSE) for the POBDS model is called the Boolean Kalman Filter. In this talk, we describe our recent research on the POBDS model, with applications in inference, fault detection, and control of gene regulatory networks.