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It has been shown in previous economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combined forecasts. The issues and methods of nonlinear combined forecasts have not yet been fully explored, even though forecast improvements may be possible using nonlinear combination techniques. We investigate the fuzzy neural network (FNN) as a tool for nonlinear combined forecasts. The performance of the networks is evaluated by comparing them to two individual forecasting methods and three conventional linear combining methods. The outcome of the comparison proved that the prediction by the FNN method generally performs better than those by individual forecasting methods, as well as linear combining methods. The paper suggests that the FNN method can be used as an alternative to conventional linear combining methods to achieve greater forecasting accuracy. Superiority of the FNN arises because of its flexibility in accounting for potentially complex nonlinear relationships not easily captured by traditional linear models.