Moving Horizon State Estimation for a bioprocesses modelled by a neural network

In this article, we propose a Moving-Horizon State-Estimation method, applied to a neural dynamical process model. Firstly, the approach chosen to represent a non-linear dynamical system by a neural network is explained. After that, the MHSE method, used to perform the state estimation, is presented. The algorithm performances are showed on a biotechnological process. The combination of the MHSE method and the neural network permits a particularly efficient estimation of the state of the process, with a nonlinear model easy to build thanks to the neural network, and with an easy timing due to the choice of the MHSE method.