A new method for adaptive model-based control of economic systems using a neuro-fuzzy-genetic approach: the case of international trade dynamics

We describe the application of our new method for adaptive model based control (using a neuro-fuzzy-genetic approach) to the problem of controlling international trade dynamics. The problem of international trade between two or more countries is a very complex one because of the nonlinearities involved in the mathematical models (O. Castillo and P. Melin, 1998). We describe the methodology to develop an intelligent system for controlling international trade that can be used by the government of a specific country to maximize the profit from its international trade with other countries. Our method for adaptive model based control of nonlinear dynamical systems consists of using a fuzzy rule base for model selection, a genetic algorithm for identification and a neural network for control (O. Melin and P. Castillo, 1998). Accordingly, an intelligent control system based on our methodology has an architecture with three main modules: model selection, identification and control. For the case of international trade, we have developed the fuzzy rule base for selecting the appropriate mathematical models for the problem, the genetic algorithm for parameter identification, and the neural network for control