DocumentCode :
1417566
Title :
A fuzzy neural network based on fuzzy hierarchy error approach
Author :
Wu, A. ; Tam, P.K.S.
Author_Institution :
Dept. of Electron. & Inf. Eng., Hong Kong Polytech., Kowloon, China
Volume :
8
Issue :
6
fYear :
2000
fDate :
12/1/2000 12:00:00 AM
Firstpage :
808
Lastpage :
816
Abstract :
This paper presents a novel fuzzy neural network which consists of an antecedent network and a consequent network. The antecedent network matches the premises of the fuzzy rules and the consequent network implements the consequences of the rules. In the network learning and training phase, a concise and effective algorithm based on the fuzzy hierarchy error approach is proposed to update the parameters of the network. This algorithm is simple to implement and it does not require as many calculations as some other classic neural network learning algorithms. A model reference adaptive control structure incorporating the proposed fuzzy neural network is studied. Simulation results of a cart-pole balancing system demonstrate the effectiveness of the proposed method
Keywords :
adaptive control; fuzzy control; fuzzy neural nets; learning (artificial intelligence); neurocontrollers; antecedent network; cart-pole balancing; consequent network; fuzzy hierarchy error; fuzzy neural network; learning algorithm; model reference adaptive control; Artificial neural networks; Error correction; Function approximation; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy reasoning; Humans; Multi-layer neural network; Neural networks;
fLanguage :
English
Journal_Title :
Fuzzy Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1063-6706
Type :
jour
DOI :
10.1109/91.890349
Filename :
890349
Link To Document :
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