A five-layer neuro-fuzzy model with Sugeno fuzzy rules is developed
to model the dynamics of airline passengers. The effectiveness in modeling,
prediction and forecasting is validated by a set of data containing the
consumer price index, the exchange rate, the gross national product, and the
number of airline passengers traveling abroad from 1995 to 2007. A
modified moving average method is applied to predict the input set for the
model in forecasting the number of airline passengers. Simulation results
show that the neuro-fuzzy model with Sugeno fuzzy rules is effective in
prediction and accurate in forecasting. The input error from the modified
moving average method is attenuated by the neuro-fuzzy model to yield
better forecasting results.