A rule based prototype system for automatic classification in industrial quality control

An architecture is presented using fuzzy inference methods and neural networks. The combined fuzzy-neural architecture extracts rules from training data and tunes its parameters to obtain optimum results by supervised learning. A prototype system is developed using the proposed architecture, for automatic classification of solder joint images. The classification results obtained are superior to the conventional classifiers and similar to the best results obtained by neural classifiers. This application shows that some of the concerns such as the need for expert knowledge in fuzzy systems and the black box nature in neural networks can be successfully overcome by using fuzzy-neural methods. Additionally, it is possible to include partial a priori knowledge into the network, and to remove superfluous input features from the system, which is a result that cannot be obtained using a conventional neural network