| Popis: |
The sparsity and low quality of measurement data in biochemical applications often make the development of black-box neural network models particularly delicate. Hence, it is appealing to resort to a hybrid physical-neural network approach, which combines a first-principles model and a partial neural network model. In this study, the hybrid approach is applied to a real case study, e.g. batch CHO animal cell cultures. Two alternative model structures are developed, in which NNs are used to describe either the reaction kinetics or the complete reaction rates (including the reaction pseudostoichiometry). Parameters and initial conditions are estimated from experimental data using a maximum likelihood approach, which takes all the measurement errors into account. A special procedure for initializing the minimization of the objective function is devised. The good model agreement is demonstrated with cross-validation tests. |
