DocumentCode
2052015
Title
Identification of drilling system using evolving recurrent fuzzy neural networks
Author
Arao, Masaki ; Kawaji, Shigeyasu
Author_Institution
Graduate Sch. of Sci. & Technol., Kumamoto Univ., Japan
Volume
3
fYear
2001
fDate
2001
Firstpage
1384
Abstract
The thrust force and cutting torque are the main output variables in designing of drilling control systems. In this paper, a method for estimating the thrust force and cutting torque in the drilling process by using recurrent fuzzy neural networks is proposed. The simulated and experimental results obtained demonstrate the effectiveness of the proposed method
Keywords
force control; fuzzy neural nets; genetic algorithms; identification; machining; recurrent neural nets; torque control; cutting torque; drilling system; evolutionary algorithms; fuzzy neural networks; identification; probabilistic incremental program evolution; random search algorithm; recurrent neural networks; thrust force control; Control systems; Drilling machines; Feeds; Force control; Force measurement; Fuzzy control; Fuzzy neural networks; Induction motors; Neural networks; Torque;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location
Tucson, AZ
ISSN
1062-922X
Print_ISBN
0-7803-7087-2
Type
conf
DOI
10.1109/ICSMC.2001.973475
Filename
973475
Link To Document