Using a diffusion wavelet neural network for short-term time series learning in the wafer level chip scale package process
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-014-0949-9
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
- Li, Der-Chang & Lin, Yao-San, 2006. "Using virtual sample generation to build up management knowledge in the early manufacturing stages," European Journal of Operational Research, Elsevier, vol. 175(1), pages 413-434, November.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Li, Der-Chiang & Lin, Liang-Sian, 2013. "A new approach to assess product lifetime performance for small data sets," European Journal of Operational Research, Elsevier, vol. 230(2), pages 290-298.
- Che-Jung Chang & Liping Yu & Peng Jin, 2016. "A mega-trend-diffusion grey forecasting model for short-term manufacturing demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1439-1445, December.
- He, Yan-Lin & Wang, Ping-Jiang & Zhang, Ming-Qing & Zhu, Qun-Xiong & Xu, Yuan, 2018. "A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry," Energy, Elsevier, vol. 147(C), pages 418-427.
- Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
- Li, Der-Chiang & Lin, Yao-San, 2008. "Learning management knowledge for manufacturing systems in the early stages using time series data," European Journal of Operational Research, Elsevier, vol. 184(1), pages 169-184, January.
- Yawen Wang & Shihua Zhang, 2023. "Prediction of Tumor Lymph Node Metastasis Using Wasserstein Distance-Based Generative Adversarial Networks Combing with Neural Architecture Search for Predicting," Mathematics, MDPI, vol. 11(3), pages 1-14, February.
More about this item
Keywords
Short-term time series; Virtual samples; Wavelet neural network; Wafer level chip scale package;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:27:y:2016:i:6:d:10.1007_s10845-014-0949-9. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.