Fluid Modelling: A Step Toward More Adequate Modelling
One of the main problems at modelling economic systems is changing of the structure of the system. It is especialy important for economic situation in Yugoslavia during last few years. It’s characteristic is drastic changing of inner relationships as a consequence of the proposed economic reform and hyperinflation trends. This means that the structure of the existing economic system is changing (the structure between main macroeconimic sectors). When the a priori informations about model are unknown, an adaptive model is needed. Adaptability means that it is possible to: (1) change the values of parameters; (2) change the structure of the model so that it corresponds to the changes of the structure of the system under control; (3) modify goals and criterions according to the additional informations about surroundings; (4) change limitations control actions due to changes of available resources. The subject of researches in this work is the “structural adaptability” that is models with unknown input-output mapping operator.
Some experiences with fluid modeling of economic systems
This paper deals with a new approach to modeling of economic systems, called fluid modeling. The main feature of this approach amounts to a revised concept of dynamics. After having established a set of requirements towards economic modeling we represent a practical example on fluid dynamic modeling of a particular macroeconomic system and discuss the extent to which the basic requirements of fluid modeling have been fulfilled.
Canonical structures in the identification of multivariable systems
In this paper a unitary identification procedure for linear multivariable systems is described. The approach considered is based on a preliminary canonical structure identification, i.e. on the direct determination, from input-output sequences, of a set of invariant indexes completely describing the input-output structure of the system. Reduced computation time and storage, canonical representation of the identified model and easy estimation of the initial state of the system are in this way achieved. The results obtained from the application of this procedure to a simulated process and to a chemical reactor are also reported.