Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication

In this paper, the fuzzy group method data handling-type (GMDH) neural networks and their application to the forecasting of mobile communication systems are described. At present, the 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 the neural-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the neuro-fuzzy GMDH. A computer program is developed and successful applications are shown in the field of estimating problems of mobile communication with a number of factors considered.