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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Becker, W. Xinghuo Yu Jiyuan Tu Lee, E. |
| Copyright Year | 2006 |
| Description | Author affiliation: R. Melbourne Inst. of Technol., Melbourne (Becker, W.; Jiyuan Tu) |
| Abstract | A fuzzy neural network enhanced with evolutionary algorithms, based on the GRNNFA, is proposed that is able to accurately predict the effects of a single compartment fire, based on experimental data. This system is shown to make predictions with within 5% accuracy, thus demonstrating that it can learn the non-linear nature of fluid dynamics. Because of its speed it is able to quickly generate views of the nature of the fire, enabling users to interrogate it and gain intelligence as to what compartment geometries lead to greater fire hazards. |
| Starting Page | 4111 |
| Ending Page | 4117 |
| File Size | 403541 |
| Page Count | 7 |
| File Format | |
| ISBN | 0780394909 |
| DOI | 10.1109/IJCNN.2006.246957 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-07-16 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fires Risk analysis Fuzzy neural networks Computational modeling Artificial neural networks Neural networks Fluid dynamics Geometry Computer simulation Algorithm design and analysis |
| Content Type | Text |
| Resource Type | Article |
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