A neural prediction of multi-sensor systems

In actual engineering a typical problem concerns the prediction (classification)of successive states of a real world system. The state is often characterized by several measures related to a multi-sensor array. We propose in the paper a clustering approach to the automatic determination of significant zones in the mulitdimensional space where data can be represented and by which the information about the characteristic system state can be classified. Using the approach we will obtain multidimensional time series, which will be validated by considering a particular application concerning the prediction of the vehicular traffic flow.