Application of a noisy data classification technique to determine the occurrence of flashover in compartment fires

This paper presents a hybrid Artificial Neural Network (ANN) model that is developed for noisy data classification. The model, named GRNNFA, is a fusion of the Fuzzy Adaptive Resonance Theory (FA) model and the General Regression Neural Network (GRNN) model. The GRNNFA model not only retains the important features of the parent models, which include stable learning, fast training, and an incremental growth network structure, but also facilitates the removal of noise that is embedded in training samples. The robustness of the GRNNFA model is demonstrated by the Noisy Two Intertwined Spirals problem and other benchmarking problems. The model is then applied to Fisher's Iris Data, which is a real-world classification problem. The results show that the percentage of correct predictions is statistically higher than in variant models of the adaptive resonance theory. The GRNNFA is further employed in a new application area of soft computing-fire dynamics, which is highly non-linear in nature. Flashover is the most dangerous scenrio in the development of a compartment fire, during which, any unburned combustible material, including the unburned soot particles inside the compartment, is ignited spontaneously and all combustible material is then simultaneously involved in the burning process. The GRNNFA model is applied to predict the occurrence of the flashover in compartment fires based on the fire size and the geometry of the fire compartment. The performance of the GRNNFA is compared with other published results, and it is shown to be statistically superior to other ANN models.