An improved Fuzzy neural networks algorithm and its application in resistance furnace

Resistance furnace has the characteristics of nonlinear, time-delay, time-varying and so on. In order to overcome the disadvantage of the traditional fuzzy neural network (FNN) controller with long setting time, an improved fuzzy neural network controller is presented in this paper. It consists of t...

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Bibliographic Details
Main Authors: Yong-song Lu, Hai-peng Pan
Format: Conference Proceeding
Language:English
Subjects:
Automatic control
compensator
Control systems
Error correction
FNN
Furnaces
Fuzzy control
Fuzzy neural networks
Fuzzy systems
Least squares methods
Mathematical model
Predictive models
recursive least square algorithm
resistance furnace
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Summary:Resistance furnace has the characteristics of nonlinear, time-delay, time-varying and so on. In order to overcome the disadvantage of the traditional fuzzy neural network (FNN) controller with long setting time, an improved fuzzy neural network controller is presented in this paper. It consists of two parts. One is the traditional fuzzy neural network. The other is a compensator, which is used to accelerate the response of the plant. Based on the recursive least square (RLS) algorithm, the parameters of the model are identified on line. According to this model, the trend of the plant can be predicted and the overshoot can be restrained. And the simulation result indicates that this method has the characteristics of short setting time and obvious improvement of traditional fuzzy neural network.
ISSN:2154-9613
DOI:10.1109/CCCM.2009.5268009