Neural networks have the advantage that they can deal with noisy, inconsistent and incomplete data.
However, convergence may be difficult for complex problems with large amount of data. Using knowledge based systems provides the possibility to store prior knowledge in a structured way, which is very hard to do with neural networks. In this project, we propose to combine the two approaches in a hybrid modular structure such that the advantages of both methods can be combined. Moreover, with the modular structure the neural networks are smaller than in a monolithic network, which makes it easier to extract rules which are developed during the training procedure. This problem requires learning fuzzy relations between data bases of travel agencies to predict holiday destinations, weather forecast in Europe, typical behaviour of various types of client groups.