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Analysis and Control of Polynomial Dynamic Models with Biological Applications synthesizes three mathematical background areas (graphs, matrices and optimization) to solve pro… Read more
SUSTAINABLE DEVELOPMENT
Save up to 30% on top Physical Sciences & Engineering titles!
Analysis and Control of Polynomial Dynamic Models with Biological Applications synthesizes three mathematical background areas (graphs, matrices and optimization) to solve problems in the biological sciences (in particular, dynamic analysis and controller design of QP and polynomial systems arising from predator-prey and biochemical models). The book puts a significant emphasis on applications, focusing on quasi-polynomial (QP, or generalized Lotka-Volterra) and kinetic systems (also called biochemical reaction networks or simply CRNs) since they are universal descriptors for smooth nonlinear systems and can represent all important dynamical phenomena that are present in biological (and also in general) dynamical systems.
GS
The history of the scientific cooperation of the proposed three authors dates back to 2003. Since then, they have published more than 25 joint scientific papers in international journals and conference proceedings mostly related to the topic of the proposed book. The scientific background of the three authors are really complementary: Prof. Katalin Hangos is an internationally known expert in the modelling and control of thermodynamical and (bio)chemical systems, Dr. Attila Magyar has significant experience in the analysis, application and control of quasi-polynomial systems, while Prof. Gábor Szederkényi has results on the optimization-based structural analysis and synthesis of kinetic systems. Moreover, all three authors have had continuous education and supervising experience both on the MSc and PhD levels at different universities.
AM
KH
Her main research interest lies in dynamic modelling of process systems for control and diagnostic purposes. She is a co-author of more than 100 papers on various aspects of modelling and control of process systems with nonlinear, stochastic, Petri net, qualitative and graph theory based models.