Abstract—The optimal control is one of the possible controllers for a dynamic system, having a linear quadratic regulator and using the Pontryagin’s principle or the dynamic programming method. Stochastic disturbances may affect the coefficients (multiplicative disturbances) or the equations (additive disturbances), provided that the shocks are not too great. Nevertheless, this approach encounters difficulties when uncertainties are very important or when the probability calculus is of no help with very imprecise data. The fuzzy logic contributes to a pragmatic solution of such a problem since it operates on fuzzy numbers. A fuzzy controller acts as an artificial decision maker that operates in a closed-loop system in real time. This contribution seeks to explore the tracking problem and control of dynamic macroeconomic models using a fuzzy learning algorithm. A two inputs- single output (TISO) fuzzy model is applied to the linear fluctuation model of Phillips and to the nonlinear growth model of Goodwin. Keywords—fuzzy control, macroeconomic model, multiplier- accelerator, nonlinear accelerator, stabilization policy. I
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