A new fuzzy-genetic approach for the simulation and forecasting of International Trade non-linear dynamics

We describe in this paper non-linear mathematical models that can be used to study the dynamics of international trade. Mathematical models of International Trade (IT), between three or more countries, can show very complicated dynamics in time (with the possible occurrence of chaotic behavior). The simulation of these models is critical in understanding the behavior of the relevant financial and economical variables for the problem of IT. Also, performing the simulations for different parameter values of the models will enable the forecasting of future IT. The problem of simulation and forecasting of IT has been solved in this paper by using Soft Computing (SC) techniques. An intelligent system for automated simulation of IT, combining fuzzy logic techniques and genetic algorithms, has been developed for the simulation and behavior identification of the mathematical models. The intelligent system uses a specific genetic algorithm to generate the best set of parameter values for performing numerical simulation of the dynamical system (of IT) and a fuzzy rule base for behavior identification. The importance of simulation and forecasting of IT can be measured if we consider that one of the goals for a specific country is to find the optimum benefit from its international trade with other countries.