吉林大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (01): 171-0175.

• paper • Previous Articles     Next Articles

Control of milling classification using system identification
and T-S fuzzy neural network

ZHAO Hong-wei1,2,QI Yi-ming1,ZANG Xue-bai1,ZHANG Xiao-lin1,MA Ying-zhe1   

  1. 1.College of Computer Science and Technology,Jilin University, Changchun 130012,China;2.Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun 130012,China
  • Received:2009-11-26 Online:2011-01-01 Published:2011-01-01

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Abstract:

Milling classification control is a slow timedelaying and nonlinear process. A control method was presented based on fuzzy neural network. To automatically acquire the fuzzy rules and the initial parameters of the algorithm, the fuzzy clustering was used in system identification. According to the received fuzzy system, an adaptivenetwork based fuzzy inference system was built on TakageSugone inference model with no professional experience, the capability of which is more adaptive and faster than traditional Fuzzy Neural Network (FNN). Computer simulation of a milling classification control process was carried out, which validates the proposed control method.

Key words: artificial intelligence, fuzzy neural network, system identification, adaptive control, milling classification control

CLC Number: 

  • TP274
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