DocumentCode :
2315391
Title :
Application of ANFIS in the design of fuzzy controller
Author :
Teng-Fei Li ; Chao-Ying Liu ; Zhe-Ying Song ; Xue-Ling Song ; Qing-qing Yan
Author_Institution :
Coll. of Electr. Eng. & Informational Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Volume :
3
fYear :
2012
fDate :
15-17 July 2012
Firstpage :
825
Lastpage :
829
Abstract :
How to generate and adjust the membership function and fuzzy rules are difficult problems in the design of fuzzy controller. To solve this problem, adaptive neural fuzzy inference system (ANFIS) is used to design the fuzzy control system, then the fuzzy rules and membership function can be obtained by back propagation or hybrid algorithm of the neural network. In order to verify the validity of the method, the distillation column temperature control system and CFBB temperature control system are simulated respectively. The results of simulation show that the membership functions and the fuzzy rules of the fuzzy controller can be obtained by the training of the PID control´s input/output data, and it can convert PID control experiences into fuzzy control rules efficiently.
Keywords :
adaptive control; backpropagation; control system synthesis; distillation equipment; fuzzy control; fuzzy neural nets; neurocontrollers; temperature control; ANFIS; CFBB temperature control system simulation; adaptive neural fuzzy inference system; back propagation; distillation column temperature control system simulation; fuzzy controller design; fuzzy rule; hybrid algorithm; membership function; Abstracts; Indium phosphide; Niobium; Adaptive neural fuzzy inference system (ANFIS); Fuzzy control; Temperature control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
ISSN :
2160-133X
Print_ISBN :
978-1-4673-1484-8
Type :
conf
DOI :
10.1109/ICMLC.2012.6359459
Filename :
6359459
Link To Document :
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