Using sensitivity functions to simulation of complex processes

The paper deals with the use of sensitivity functions that allow us to develop mathematical models of complex processes using short-time experimental samples. The process is said to be complex if variables which describe the states of the process in time are interrelated. The models of complex processes proposed in the paper are presented in the form of regression equations which can be used for the analysis of mutual influences of process variables as well as for the short-time prediction of future process states. The discussed approach is based on the assumption that the process to be studied exhibits the regularity property. As is shown in the paper, under this condition, it is sufficient to have five or six experimental samples to start synthesis of models which can be further modified during simulation.