Paper Title:
Optimization Technique for Neural Network-Based Error Compensation in CNC Machining
  Abstract

The neural network (NN) is extensively used for error predication and compensation in CNC machining. However, the training samples are finite and have some noises which limit the training accuracy of the neural network. Furthermore, the weight matrixes and the valve values of the NN are fixed which limit the generalization performance of the trained NN. To solve the problems, some optimization techniques are proposed in this paper. A standardized formula is proposed to standardize the training samples. The data filter is designed to eliminate the noise. A correction strategy is proposed to realize the generalization performance of the trained NN.

  Info
Periodical
Advanced Materials Research (Volumes 189-193)
Edited by
Zhengyi Jiang, Shanqing Li, Jianmin Zeng, Xiaoping Liao and Daoguo Yang
Pages
1878-1881
DOI
10.4028/www.scientific.net/AMR.189-193.1878
Citation
K. G. Fan, J. G. Yang, "Optimization Technique for Neural Network-Based Error Compensation in CNC Machining", Advanced Materials Research, Vols. 189-193, pp. 1878-1881, 2011
Online since
February 2011
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Price
$35.00
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