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Controlling International Trade Dynamics

  • Oscar Castillo
  • Patricia Melin
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 63)

Abstract

We describe in this chapter 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 (Castillo and Melin, 1998). In this chapter, 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 non-linear dynamical systems consists of using a fuzzy rule base for model selection, a genetic algorithm for identification and a neural network for control (Melin and 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.

Keywords

Genetic Algorithm Interest Rate International Trade Fuzzy Inference System Decision Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • Oscar Castillo
    • 1
  • Patricia Melin
    • 1
  1. 1.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA

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