Moving Horizon State Estimation for a bioprocesses modelled by a neural network
- 1 December 1997
- journal article
- research article
- Published by SAGE Publications in Transactions of the Institute of Measurement and Control
- Vol. 19 (5) , 263-270
- https://doi.org/10.1177/014233129701900506
Abstract
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 nonlinear 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 tuning due to the choice of the MHSE method.Keywords
This publication has 10 references indexed in Scilit:
- Dynamic process modeling with recurrent neural networksAIChE Journal, 1993
- Neural networks for control systems—A surveyAutomatica, 1992
- On‐line prediction of fermentation variables using neural networksBiotechnology & Bioengineering, 1990
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- On the number of samples necessary to achieve observabilitySystems & Control Letters, 1981
- Observers for bilinear systems with bounded input†International Journal of Systems Science, 1979
- An introduction to observersIEEE Transactions on Automatic Control, 1971
- New Results in Linear Filtering and Prediction TheoryJournal of Basic Engineering, 1961
- A logical calculus of the ideas immanent in nervous activityBulletin of Mathematical Biology, 1943