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, P...

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Bibliographic Details
Published in:Advanced engineering informatics 2002-07, Vol.16 (3), p.179-191
Main Authors: Lee, E.W.M., Yuen, R.K.K., Lo, S.M., Lam, K.C.
Format: Article
Language:English
Subjects:
Entropy
Flashover
Fuzzy ARTMAP
PEmap
Probabilistic inference
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Summary: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.
ISSN:1474-0346
1873-5320
DOI:10.1016/S1474-0346(02)00009-5