NDLI: Cutting error prediction by multilayer neural networks for machine tools with thermal expansion and compression

Content Provider	IEEE Xplore Digital Library
Author	Nakayama, K. Hirano, A. Katoh, S. Yamamoto, T. Nakanishi, K. Sawada, M.
Copyright Year	2002
Description	Author affiliation: Fac. of Eng., Kanazawa Univ., Japan (Nakayama, K.; Hirano, A.; Katoh, S.)
Abstract	In training neural networks, it is important to reduce input variables for saving memory, reducing network size, and achieving fast training. The paper proposes two kinds of selecting methods for useful input variables. One of them is to use information of connection weights after training. If a sum of absolute value of the connection weights related to the input node is large, then this input variable is selected. In some cases, only positive connection weights are taken into account. The other method is based on correlation coefficients among the input variables. If a time series of the input variable can be obtained by amplifying and shifting that of another input variable, then the former can be absorbed in the latter. These analysis methods are applied to predicting cutting error caused by thermal expansion and compression in machine tools. The input variables are reduced from 32 points to 16 points, while maintaining good prediction within 6/spl mu/m, which can be applicable to real machine tools.
Sponsorship	IEEE IEEE Neural Networks Soc. (NNS) Int. Neural Network Soc
Starting Page	1373
Ending Page	1378
File Size	475580
Page Count	6
File Format	PDF
ISBN	0780372786
ISSN	10987576
DOI	10.1109/IJCNN.2002.1007716
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-layer neural network Neural networks Machine tools Thermal expansion Input variables Industrial training Diseases Error correction Temperature measurement Equations
Content Type	Text
Resource Type	Article

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