Estimation of cutting torque in drilling system based on flexible neural network

Author

Kim, Myeonghee ; Matsunaga, Nobutoma ; Kawaji, Shigeyasu

Author_Institution

Graduate Sch. of Sci. & Technol., Kumamoto Univ., Japan

Volume

1

fYear

2003

fDate

20-24 July 2003

Firstpage

642

Abstract

The use of conventional neural network in complex control applications has been limited due to its structure of low degree of freedom. In this paper, a flexible neural network (FNN), which gives flexibility of activation function, is introduced to improve the ability of the neural network, and a learning algorithm is derived. In order to show the effectiveness, the proposed method is applied to the estimation of cutting torque in a drilling process, which is a complicated control problem.

Keywords

computational complexity; cutting; drilling; learning (artificial intelligence); multilayer perceptrons; process control; activation function flexibility; complicated control problems; cutting torque estimation; drilling system; flexible neural network; learning algorithm; multilayered neural network; sigmoid function shape; sigmoid function slope; Drilling; Electrical equipment industry; Error correction; Feeds; Fuzzy control; Industrial control; Intelligent networks; Multi-layer neural network; Neural networks; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223439

Filename

1223439