Genetic Algorithm Approach for the Identification Problem of the Discrete Possibilistic Dynamic System

This work deals with the problem of identification of Discrete Possibilistic Dynamic System (DPDS), using the technologies of Genetic Algorithmms (GA). Applying the results from [5-9,11-13,15,16,18-20], the fuzzy recurrent process with possibilistic uncertainty, the source of which is expert knowledge reflections on the states of evolutionary complex extremal system, is constructed. The dynamics of DPDS is described and the constructed model is converted to the finite model. Based on the fuzzy-integral model a genetic algorithm approach is developed for identifying the transition operation of the DPDS finite model. The DPDS transition operator is restored by means of expert data with possibilistic uncertainty. Obtained results are illustrated by the example for prediction and stopping problems for evaluations of the increasing business risks. Key-Words: DPDS; identification of DPDS; genetic algorithm, solution regularization principle