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.
Some New Transformation Algorithms in the Identification of Continuous-Time Multivariable Systems Using Discrete Identification Methods
Abstract A procedure for the identification of linear continuous multivariable systems is shown which is based on a discrete identification method and suitable transformation algorithms. Both, the structural and the parametric identification are performed from samples of input and output data, and are based on the discrete input-output model. A new method for the deduction of a state-space model from the identified input-output model is proposed. The method is based on a recursive formula by which construction and inversion of the auxiliary matrix is avoided. The discrete state-space model is transformed into the continuous state-space model using methods based on the fundamental matrix logarithm and its approximations. Four different algorithms for computation of the matrix logarithm function are proposed. For the derivation of the control matrix and the transmission matrix, also four different approaches are shown: the step-invariant, the rampinvariant, the bilinear and the combined approach. The proposed procedure is very efficient when sequences of steps or rectangular pulses can be used as test functions in the identification procedure. If normal operating records of the plant are used, the method is less efficient. The applicability of the proposed procedure for systems simplification is also discussed. Finally, an application of this procedure to a simulated continuous process, using different transformation methods is given.