 Maximum likelihood parameter estimation of a hybrid neural-classical structure for the simulation of bioprocessesA. Hanomolo, Ph. Bogaerts, J. Graefe, M. Cherlet, J. Wérenne and R. Hanus Mathematics and Computers in Simulation (MATCOM), 2000, vol. 51, issue 3, 375-385 Abstract: This paper proposes a hybrid structure for the modeling of a bioprocess: classical (in the form of a priori knowledge describing the mass balances) and neural (a radial basis function network describing the nonlinear reactions kinetics within these mass balances). The aim is to build a continuoussimulator capable to reconstruct from initial conditions the trajectory of state variables (i.e. the main component concentrations) by considering also an aspect which usually is not taken into account in bioprocess modeling: the existence of important measurement errors. A clustering strategy is used for placing the Gaussian centers and a maximum likelihood cost function is defined for the estimation of the network weights and initial conditions for the simulator. The structure is tested on batch animal cell cultures for which rare and asynchronous measurements are available: glucose, glutamine, lactate and biomass concentrations. Keywords: Neural-classical structure; Maximum likelihood cost function; Bioprocesses (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:51:y:2000:i:3:p:375-385 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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