NDLI: The Research on the Model of Flatness Control Based on the Optimized RBF Fuzzy Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Hai-Tao He Lan Zhang
Copyright Year	2007
Description	Author affiliation: Yanshan Univ., Qinhuangdao (Hai-Tao He; Lan Zhang)
Abstract	As there are many non-linear elements influencing the flatness in the rolling process and its accurate mathematics model is too complex to build, a fuzzy neural network controller is proposed for the cold rolled flatness control. Fuzzy neural controller does not require accurate model of plant and is able to learn to control adaptively. RBF network is adopted in the fuzzy neural network. To automatically acquire the fuzzy rule-base and the initial parameters of the RBF fuzzy model, the relationship clustering method is used in structure identification. Based on the clustering result, a fuzzy neural network is set up and then trained by genetic algorithm to obtain a precise flatness control model. The simulation result shows that it not only reduces the complexity of neural network, but also has faster convergence rate and less possibility to local minimum. The response is more favorable than that of conventional fuzzy controllers and that of fuzzy neural network based on BP network.
Starting Page	472
Ending Page	476
File Size	585359
Page Count	5
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370191
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Fuzzy control Automatic control Mathematics Mathematical model Radial basis function networks Clustering methods Genetic algorithms Neural networks Convergence Relation clustering method Fuzzy neural network Flatness control
Content Type	Text
Resource Type	Article

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