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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dong Zengshou Zhang Gang Li Meiling |
| Copyright Year | 2007 |
| Description | Author affiliation: Taiyuan Univ. of Technol., Taiyuan (Dong Zengshou; Zhang Gang) |
| Abstract | In this paper, a new flame stability identifying method was suggested whereas the problem of flame stability recognizing of high temperature air combustion (HTAC). The primary idea of this proposed method is to diagnosing the flame stability by an improved self-adaptive wavelet neural network (AWNN), which is learned from an improved particle swarm optimization based on self-adaptive escape velocity (AEPSO). Calculating results are identical to the simulation results, which illuminate the feasibility of the proposed algorithm and provide a theatrical basis for the development of the high temperature air combustion technology. |
| Starting Page | 1304 |
| Ending Page | 1307 |
| File Size | 352212 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769530389 |
| DOI | 10.1109/ICCIT.2007.46 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-11-21 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Temperature Image recognition Neural networks Stability criteria Fires Gray-scale Combustion Particle swarm optimization Information technology Pixel |
| Content Type | Text |
| Resource Type | Article |
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