Civil Aviation Passenger Traffic Volume Forecasting Based on Fuzzy Diagonal Regression Neural Networks

In view of the characteristics of our civil aviation, a fuzzy diagonal regression neural networks recurrent forecast model was proposed based on analyzing influential factors of passenger traffic volume. This model deals with the uncertain factors fuzzily and certainty factors using normalization in the front network layer, which solved the problem for inconsistent of importing dimension effectively. At the same time, Example proves the validity of the model. Practice proves that applying fuzzy diagonal regression neural networks recurrent forecast model to civil aviation passenger traffic volume is practicable, precise and universal, compared with the other methods such as the support vector regression, BP neural networks etc.