A Self-Organized Neuro-Fuzzy System for Air Cargo and Airline Passenger Dynamics Modeling and Forecasting

Abstract

A self-organized, five-layer neuro-fuzzy model is developed to model the dynamics and to forecast air cargo and airline passenger by using the input of previous years’ consumer price index, exchange rate, gross national product, and number of cargo volume/passenger traffic. Simulation results show that the neuro-fuzzy model is more effective than neural network in prediction and accurate in forecasting. The effectiveness in modeling, prediction and forecasting is validated, and the input error from the series-parallel identification method is attenuated by the neuro-fuzzy model to yield better forecasting results.