A virtual-sample technology based artificial-neural-network for a complex data analysis in a glass-ceramic system
点击次数：发表时间：2025-05-23
- 所属单位：[1]Univ Elect Sci & Technol China, State Key Lab Elect Thin Films & Integrated Devic, Chengdu 610054, Peoples R China;[2]Shenzhen Univ, Normal Coll, Shenzhen 518060, Guangdong, Peoples R China
- 发表刊物：JOURNAL OF CERAMIC PROCESSING RESEARCH
- 关键字：artificial neural network; material data analysis; virtual sample technology; slag glass-ceramics
- 摘要：Artificial neural network has becoming a mainstream technology in the domain of complex materials data analysis. Based on a slag glass-ceramic system we brought forward a virtual sample technology to increase the training samples by fluctuating the content of main compositions in a proper small amplitude. Simulation results proved that a good virtual sample set can not only improve the network＇s prediction ability considerably, but can also suppress the "overtraining" phenomenon. Therefore a virtual sample improved neural network model can learn the relationship from a small size experimental data set and give an accurate and stable prediction for the test samples. This is more helpful to the material data analysis and can facilitate the design and development for new materials.
- 文献类型：Article
- 卷号：9
- 期号：4
- 页面范围：393-397
- ISSN号：1229-9162
- 是否译文：否
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- 性别：男
- 职称：教授
- 毕业院校：电子科技大学
- 学历：博士研究生毕业
- 学位：工学博士学位
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- 2012 当选： 新世纪优秀人才计划
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