Nonlinear filtering for a Chemostat model
Abstract
We aim in this paper to estimate the state variables of a stochastic Chemostat model using three methods, the extended Kalman filter, the unscented Kalman filter and the particle filter(bootstrap). The results obtained from these three algorithms are compared to choose which one of them is the best method for state estimation for this type of systems.
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