DOI
stands for Digital Object Identifier
(
D
igital
O
bject
I
dentifier
)
,
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
「
http://dx.doi.org/
」
before a DOI.
For instance, if the DOI of an article is
10.5297/ser.1201.002
, you can link persistently to the article by entering the following link in your browser:
http://dx.doi.org/
10.5297/ser.1201.002
。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration ( doi.airiti.com ) 。
Application of regression neuron in predicting surface roughness in end milling operations
張竹賢 , Masters Advisor:黃博滄
繁體中文
DOI:
10.6840/CYCU.2010.00393
迴歸分析 ; 類神經網路 ; 預測 ; 表面粗糙度 ; 銑削加工 ; regression ; surface roughness ; predict ; end milling ; artificial neural network


- [2] Chen, J.C., M.S. Lou, Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operations, Int. J. Comput. Integr. Manuf. 13 (4) (2000) 358–368.
連結: - [3] Huang, B.P., Chen, J.C., An in-process neural network-based surface roughness prediction system using a dynamometer in end milling operations, Int. J. Adv. Manuf. Technol. 21 (2003) 339–347.
連結: - [4] Ho ,W.H., Tsai, J.T., Lin, B.T., Chou, J.H., Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm, Expert Systems with Applications, 36 (2009) 3216–3222
連結: - [5] Karayel, D., Prediction and control of surface roughness in CNC lathe using artificial neural network. J. Mater. Process. Tech (2008).
連結: - [6] SubbaNarasimha, P. N., Arinze, B., & Anandarajan, M. The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: Exploration of some issues. Expert Systems with Applications, 19 (2000) 117–123.
連結: