Container Terminal Demand Forecasting Framework Using Fuzzy-GMDH and Neural Network Method

In this paper, a fuzzy group method data handling- type (GMDH) neural networks and their application to the container terminal demand forecasting of port transportation system are described. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neural-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the neural-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. The related computer program is developed and successful applications are shown in the field of estimating problem of container terminal demand with the number of factors considered.