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dc.contributor.authorZALDIVAR-COMENGES Jose' manuelen_GB
dc.date.accessioned2010-02-25T16:40:41Z-
dc.date.available1997-06-12en_GB
dc.date.available2010-02-25T16:40:41Z-
dc.date.issued1998en_GB
dc.date.submitted1997-05-27en_GB
dc.identifier.citationChemical Engineering and Processing vol. 37 p. 149-161en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC15075-
dc.description.abstractAltough nonlinear inverse and predictive control techniques based on artificial neural netwotks have been extensively applied to nonlinear systems, their use in real time applications is generally limeted. In this paper neural inverse and predictive control systems have been applied to the real- time control of the heat transfer fluid temperature in a pilot chemical reactor. The training of the inverse control system is carried out using both, generalised and specialised learning. This allows the preparation of weights of the controller acting in real-time and appropriate performances of inverse neural controller can be achieved, The predictive control system makes use of a neural network to calculate the control action . Thus, the problems related to the high computational effort involved in nonlinear model-predictive control systems are reduced. The performance of the neural controllers is compared against the PID conntroller currently installed in the plant. The results show that neural-based controllers improve the performance of the real plant.en_GB
dc.description.sponsorshipJRC.(ISIS)-Institute For Systems, Informatics And Safetyen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.relation.ispartofseriesJRC15075en_GB
dc.titleApplication of Recurrent Neural Networks in Batch Reactors. Part II. Non-linear Inverse and Predictive Control of the Heat Transfer Fluid Temperature.en_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Joint Research Centre Historical Collection

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