Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation-Reference-Cited by-同舟云学术

Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation

Published:2015-07-09 Issue:4 Volume:11 Page:505-515
ISSN:1735-5702
Container-title:Journal of Industrial Engineering International
language:en
Short-container-title:J Ind Eng Int

Author:

Maleki Mohammad Reza,Amiri Amirhossein,Mousavi Seyed Meysam

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering

Link

http://link.springer.com/content/pdf/10.1007/s40092-015-0117-7.pdf

Reference22 articles.

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2. Ahmadzadeh F, Lundberg J, Strömberg T (2013) Multivariate process parameter change identification by neural network. Int J Adv Manuf Technol 69(9–12):2261–2268

3. Allahyari S, Amiri A (2011) Clustering approach for change point estimation in multivariate normal processes. In: Proceedings of the 41st International Conference on Computers & Industrial Engineering, 38–43

4. Amiri A, Allahyari S (2012) Change point estimation methods for control chart postsignal diagnostics: a literature review. Qual Reliab Eng Int 28(7):673–685

5. Amiri A, Niaki STA, Moghadam AT (2014) A probabilistic artificial neural network-based procedure for variance change point estimation. Soft Comput 19:1–10

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