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
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;
Conference_Titel :
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-7087-2
DOI :
10.1109/ICSMC.2001.973475
