Probabilistic inference with maximum entropy for prediction of flashover in single compartment fire

This paper presents the development of a new artificial neural network model using probabilistic mapping with maximum entropy (PEmap). By maximizing the entropy of the conditional probabilities between the input and output vectors, it can carry out prediction with minimum prejudice. In this study, PEmap was employed as a binary classifier for prediction of flashover occurrence in single compartment fire. The results are compared and verified against data obtained by fuzzy ARTMAP (FAM). The performance of PEmap in this study agrees extremely well with that of FAM.