Knowledge Extraction from Artificial Neural Networks and Applications

Abstract

Knowledge acquisition is a frequent bottleneck in artificial intelligence applications. Neural learning may offer a new perspective in this field. Using Self-Organising Neural Networks, as the Kohonen model, the inherent structures in high-dimensional input spaces are projected on a low dimensional space. The exploration of structures resp. classes is then possible applying the U-Matrix method for the visualisation of data. Since Neural Networks are not able to explain the obtained results, a machine learning algorithm sig* was developed to extract symbolic knowledge in form of rules out of subsymbolic data. Combining both approaches in hybrid system results in a powerful method to solve classification and diagnosis problems. Several applications have been used to test this method. Applications on processes with dynamic characteristics, such as chemical processes and avalanche forecasting show that an extension of this method from static to dynamic data is feasible.