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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ping Ma Hai-Lian Du Feng Lv |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. of Autom., North China Electr. Power Univ., Baoding, China (Ping Ma; Hai-Lian Du) |
| Abstract | In the fossil power plant, it is rather difficult to measure the coal mass of the coal mill exactly, in order to make the coal mill work on the optimal active state, multi-sensor are used to fuse multiple signal, and the qualitative estimation of the coal mass is gotten from the algorithm. The neural network has the ability of self-organize, self-learn, and disposing the nonlinear problems, strong fault tolerant and robustness, D-S evidential theory can solve the uncertainty problem, but the evident is hard to get. Two-step fusion method combined the merit of the neural network and the evidential theory, the neural network is on the first step, the second step uses the normalization result as the evident. When this algorithm is simulated on the computer, the result proves that the method can estimate the coal mass qualitatively, according to the historical record of coal mill. |
| Sponsorship | IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany |
| Starting Page | 1307 |
| Ending Page | 1311 |
| File Size | 293758 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780390911 |
| DOI | 10.1109/ICMLC.2005.1527145 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-08-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Milling machines Neural networks Power generation Power measurement Fuses State estimation Fault tolerance Robustness Uncertainty Computational modeling D-S evidential theory Information fusion neural network coal mill |
| Content Type | Text |
| Resource Type | Article |
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