Learning algorithm by reinforcement signals for the automatic recognition system

The visual inspection of the industrial product copes with defects that have wide variety of features in the shape, size, and strength. Most of the learning algorithms of the recognition system require specific training patterns for learning of the feature extraction filters. However, there are many cases that the recognition tasks don´t have specific training patterns. We propose a learning algorithm, which reconstructs feature extraction fillers on the basis of reinforcement signals. The recognition system constructed by the learning algorithm is robust against environmental variation.