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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hui Cao Gangquan Si Yanbin Zhang Xikui Ma |
| Copyright Year | 2007 |
| Description | Author affiliation: Xi'an Jiao Tong Univ., Xi'an (Hui Cao; Gangquan Si; Yanbin Zhang; Xikui Ma) |
| Abstract | For ball mill pulverizing system of the thermal power plant, a hybrid controller of self-optimizing algorithm and adaptive neuro-fuzzy inference system(ANFIS) is proposed. In order to keep the ball mill pulverizing system working at the optimum point all along, the self-optimizing algorithm is presented. The self-optimization algorithm can automatically find out the extreme point and adjust the control set values in time. The adaptive neuro-fuzzy inference system, which integrates the advantages of the neural network and the fuzzy control, uses the learning ability of the neural network to optimize the membership functions and fuzzy logic rules of fuzzy control. Such combined framework makes fuzzy control more systematic and less relying on expert knowledge. Simulations results verify that the controller can control the ball mill pulverizing system effectively and has higher control quality. |
| Starting Page | 3289 |
| Ending Page | 3294 |
| File Size | 228337 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424408276 |
| DOI | 10.1109/ICMA.2007.4304089 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-05 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Control systems Ball milling Inference algorithms Fuzzy control Automatic control Adaptive systems Programmable control Adaptive control Neural networks Power generation Adaptive Neuro-Fuzzy Inference System Ball Mill Pulverizing System Hybrid Controller Self-Optimizing Algorithm |
| Content Type | Text |
| Resource Type | Article |
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