It focusses on problems related to an automatic analysis of sensor based data retrieved in observing the physical environment. The goal of the project, started in 1988, is to study neural networks as a tool for pattern recognition. Moreover, they sometimes perform surprisingly good in relation to the complexity of the measurement input data used for training. What is lost if certain disadvantages are removed, e.g. what is the result for the robustness if a faster training procedure is used. The emphasis of the research is on theoretical investigations on feed forward networks, the back-propagation training algorithm and its derivatives.
Extensive use is made of simulations.
Results are compared with those of traditional techniques. However, also real world problems are studied in order to find out to what extend neural nets disclose a new type of applications.