DocumentCode :
2602064
Title :
Control system using fuzzified input neural network
Author :
Treesatayapun, C. ; Uatrongjit, S. ; Likit-Anurucks, K. ; Kantapanit, K.
Author_Institution :
Dept. of Electr. Eng., Chiang Mai Univ., Chiangmai, Thailand
Volume :
2
fYear :
2002
fDate :
2002
Firstpage :
163
Abstract :
In this paper, a new fuzzy neural network structure called the fuzzified input neural network (FINN) is presented. The FINN structure is derived based on human knowledge in the form of fuzzy if-then rules. The initial setting of its parameters can be chosen intuitively from expert experience. These parameters are then adaptively adjusted during system operation. Comparisons between the proposed network and the conventional Mamdani fuzzy inference are described. The performance of FINN is demonstrated by using it as the controller for the single inverted pendulum plant. Some simulation results are given.
Keywords :
adaptive control; fuzzy control; fuzzy neural nets; inference mechanisms; neurocontrollers; nonlinear control systems; pendulums; FINN; Mamdani fuzzy inference; RBF network; adaptive control; adaptively adjusted parameters; control system; fuzzified input neural network; fuzzy if-then rules; fuzzy neural network structure; human knowledge; initial parameter setting; simulation; single inverted pendulum plant controller; system operation; Adaptive control; Control systems; Electronic mail; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Humans; Neural networks; Radial basis function networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 2002. APCCAS '02. 2002 Asia-Pacific Conference on
Print_ISBN :
0-7803-7690-0
Type :
conf
DOI :
10.1109/APCCAS.2002.1115145
Filename :
1115145
Link To Document :
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