Hierarchical approach for parameter identification of multiparameter models

The problem of parameters identification in multiparameter models of the complicated kinetics processes is analyzed. In this case the minimization of the least square function is very difficult because it is multiextremal. The problem solution needs to be obtained very well initial parameters values. A polynomial approximation of the experimental data permits to propose a hierarchical approach for obtaining initial parameters values in the global minimum area, using a consecutive approximations method. The method for parameter identification of multiparameter models is tested for modeling of fermentation systems. The model parameter values are obtained on the bases of real experimental data. The results obtained show a decrease of the model error variance on every next hierarchical level and a good agreement with the experimental data on the last level.