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Content Provider | IEEE Xplore Digital Library |
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Author | Zhennan Hong Yifa Sheng Junhong Li Kasuga, M. Liang Zhao |
Copyright Year | 2006 |
Description | Author affiliation: Dept. of Electr. Eng., NanHua Univ., Hengyang (Zhennan Hong; Yifa Sheng; Junhong Li) |
Abstract | Aimed at the characteristics of strong nonlinearity, uncertainty, time-variance and serious interconnection etc for AC electric arc-furnace (EAF). A new control method is put forward, in which fuzzy-neural network is applied. Mamdani fuzzy model is adopted in the control system, its structure has four layers. According to the gradient descent strategy, the variable learning speed algorithm of the off-line is used to seek for the better parameters of membership function and the weights of the links between layer III and layer IV, the invariable learning speed algorithm of the on-line is used to search for their optimization parameters. The unbalanced technical question of the AC EAF's three phase current is solved by fuzzy-neural control method. At the same time, the hard structure and soft structure of system are introduced in this paper. It has better robustness and real-time ability in running system |
Starting Page | 2459 |
Ending Page | 2464 |
File Size | 157331 |
Page Count | 6 |
File Format | |
ISBN | 1424404657 |
DOI | 10.1109/ICMA.2006.257737 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2006-06-25 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Control systems Fuzzy neural networks Fuzzy control Electrodes Electric variables control Temperature Adaptive control Furnaces Regulators Uncertainty weights EAF fuzzy neural network Mamdani fuzzy model membership function |
Content Type | Text |
Resource Type | Article |
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