@inproceedings{oai:kanazawa-u.repo.nii.ac.jp:00007460,
 author = {Nakayama, Kenji and Hirano, Akihiro and Katoh, Shinya and Yamamoto, Tadashi and Nakanishi, Kenichi and Sawada, Manabu},
 book = {Proceedings of the International Joint Conference on Neural Networks},
 month = {May},
 note = {In training neural networks, it is important to reduce input variables for saving memory, reducing network size, and achieving fast training. This 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 case, 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 ﾎｼm, which can be applicable to real machine tools.},
 pages = {1373--1378},
 publisher = {IEEE(Institute of Electrical and Electronics Engineers)},
 title = {Cutting error prediction by multilayer neural networks for machine tools with thermal expansion and compression},
 volume = {2},
 year = {2002}
}