Extraction of if-then rules from trained neural network and its application to earthquake prediction

This paper presents a supervised ART neural network named impulse force based ART (IFART) neural network. It enhances the prediction accuracy of the supervised ART neural network using impulse forces on attributes optimized by genetic algorithm, which identify the different effect of input attributes on category results. However, the IFART neural network is still a black box and difficult to understand, which is the disadvantage of artificial neural network. In this paper, a method to extract if-then rules from the trained IFART neural network according to its architecture is proposed to interpret the neural network. Furthermore, the rules are refined in terms of their used frequency. Finally, IFART neural network is applied to predict the magnitude of earthquake.