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 ) 。
多層感知器對輸入與權值誤差的敏感度分析及倒傳遞(BP)演算法與進化策略(ES)演算法的改善
楊盛松 , Ph.D Advisor:賀嘉律
英文
敏感度 ; 多層感知器 ; 倒傳遞演算法 ; 進化策略演算法 ; evolutionary strategy ; back-propagation ; multilayer perceptron ; sensitivity


- [1] M. Stevenson, R. Winter, and B. Widrow, “Sensitivity of feedforward neural networks to weight errors,” IEEE Trans. Neural Networks, vol. 1, pp. 71-80, Mar. 1990.
連結: - [3] S. W. Piche, “The selection of weight accuracies for Madalines,” IEEE Trans. Neural Networks, vol. 6, pp. 432-445, Mar. 1995.
連結: - [4] S. Hashem, “Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions,” in Proc. IJCNN’92, vol. 1, Baltimore, MD, 1992, pp. 419-424.
連結: - [5] L. Fu and T. Chen, “Sensitivity analysis for input vector in multilayer feedforward neural networks,” in Proc. IEEE Int. Conf. Neural Networks, vol. 1, San Francisco, CA, 1993, pp. 215-218.
連結: - [6] J. M. Zurada, A. Malinowski, and S. Usui, “Perturbation method for deleting reduntant inputs of perceptron networks,” Neurocomput., vol. 14, pp. 177-193, 1997.
連結: