Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12737
Title: Application of a noisy data classification technique to determine the occurrence of flashover in compartment fires
Authors: Lee, EWM
Lee, YY
Lim, CP
Tang, CY 
Keywords: Compartment fire
Flashover
Fuzzy ART
General regression neural network
GRNNFA
Issue Date: 2006
Source: Advanced engineering informatics, 2006, v. 20, no. 2, p. 213-222 How to cite?
Journal: Advanced Engineering Informatics 
Abstract: 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.
URI: http://hdl.handle.net/10397/12737
ISSN: 1474-0346
DOI: 10.1016/j.aei.2005.09.002
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

12
Last Week
0
Last month
0
Citations as of Oct 8, 2017

WEB OF SCIENCETM
Citations

12
Last Week
0
Last month
0
Citations as of Oct 17, 2017

Page view(s)

47
Last Week
1
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.