The Generalized Adaptive Resonance Theory (GART) neural network is developed based on an integration of Gaussian ARTMAP and the Generalized Regression Neural Network. As in our previous work [13], GART is capable of online learning and is effective in tackling both classification and regression tasks. In this paper, we further propose an Ordered–Enhanced GART (EGART) network with pruning and rule extraction capabilities. The new network, known as O–EGART–PR, is equipped with an ordering algorithm that determines the sequences of training samples, a Laplacian function, a new vigilance function, a new match-tracking mechanism, and a rule extraction procedure. The applicability of O–EGART–PR to pattern classification and rule extraction problems is evaluated with a problem in fire dynamics, i.e., to predict the occurrences of flashover in a compartment fire. The outcomes demonstrate that O–EGART–PR outperforms other networks and produces meaningful rules from data samples.
