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Integrated Ferroelectrics
An International Journal
Volume 212, 2020 - Issue 1
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Articles

The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination

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Pages 135-146 | Received 15 May 2020, Accepted 11 Aug 2020, Published online: 11 Nov 2020

References

  • L. N. de Casto, and F. J. Von Zuben, An evolutionary immune network for data clustering, Sixth Brazilian Symposium on Neural Networks, Rio de Janeiro, RJ, Brazil, 2000. In Proceedings vol. 1, 2000, pp. 84–89. DOI: 10.1109/SBRN.2000.889718.
  • C. H. Chen, editor, Neural Networks in Pattern Recognition and Their Applications (World Scientific, River Edge, NJ, 1991).
  • S. Tamura, An analysis of a noise reduction neural network, international conference on acoustics, speech, and signal processing, May 1989, Proceedings IEEE, Glasgow, Scotland, pp. 2001–2004. DOI: 10.1109/ICASSP.1989.266851.
  • N. Altinkok, and R. Koker, Mixture and pore volume fraction estimation in Al2O3/SiC ceramic cake using artificial neural networks, Mater. Des. 26 (4), 305 (2005). DOI: 10.1016/j.matdes.2004.06.012.
  • M. Song, and Y. Wang, A study of granular computing in the agenda of growth of artificial neural networks, Granul. Comput. 1 (4), 247 (2016). DOI: 10.1007/s41066-016-0020-7.
  • S.-C. Ngan et al., Node merging in Kohonen’s self-organizing mapping of fMRI data, Artif. Intell. Med. 25, 9 (2002). DOI: 10.1016/S0933-3657(02)00006-4.
  • C. Jin, C, and L. Wang, Dimensionality dependent PAC-Bayes margin bound, in Advances in Neural Information Processing Systems, 2, 1034 (2012).
  • J. Schmidhuber, Deep learning in neural networks: An overview, Neural Netw. 61, 85 (2015). DOI: 10.1016/j.neunet.2014.09.003.
  • F. Rosenblatt, Principles of Neurodynamics: Perceptions and the Theory of Brain Mechanism (Spartan Books, Washington, DC, 1961).
  • N. Hecht, Theory of the backpropagation neural network, International Joint Conference on Neural Networks, Washington, DC, USA, 1989, pp. 593–605. DOI: 10.1109/IJCNN.1989.118638.
  • I. Goodfellow, Y. Bengio, and A. Courville, Back-Propagation and Other Differentiation Algorithms in Deep Learning (MIT Press, Cambridge, MA, 2016, pp. 200–220).
  • J. Živko-Babić et al., Estimation of chemical resistance of dental ceramics by neural network, Dent. Mater. 24 (1), 18 (2008). DOI: 10.1016/j.dental.2007.01.008.
  • R. Kondo et al., Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics, Acta Mater. 141, 29 (2017). DOI: 10.1016/j.actamat.2017.09.004.
  • J. Yu, H. Wang, and J. Zhang, Neural network modeling and analysis of gel casting preparation of porous Si3N4 ceramics, Ceram. Int. 35 (7), 2943 (2009). DOI: 10.1016/j.ceramint.2009.04.008.
  • J. Li, J et al., Prediction of mechanical properties of β-Sialon ceramics based on bp neural network, Metabk 57, 265 (2018).
  • H. S. Rao, and A. Mukherjee, Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites, Comput. Mater. Sci. 5 (4), 307 (1996). DOI: 10.1016/0927-0256(95)00002-X.
  • D. J. Scott et al., Prediction of the functional properties of ceramic materials from composition using artificial neural networks, J. Eur. Ceram. Soc. 27 (16), 4425 (2007). DOI: 10.1016/j.jeurceramsoc.2007.02.212.
  • K. Shirvanimoghaddam et al., Effect of B4C, TiB2 and ZrSiO4 ceramic particles on mechanical properties of aluminium matrix composites: experimental investigation and predictive modelling, Ceram. Int. 42 (5), 6206 (2016). DOI: 10.1016/j.ceramint.2015.12.181.
  • A. H. Pakseresht et al., Development empirical-intelligent relationship between plasma spray parameters and coating performance of Yttria-Stabilized Zirconia, Int. J. Adv. Manuf. Technol. 76 (5–8), 1031 (2015). DOI: 10.1007/s00170-014-6212-x.
  • D. Guo et al., Modeling and analysis of the electrical properties of PZT through neural networks, J. Eur. Ceram. Soc. 23 (12), 2177 (2003). DOI: 10.1016/S0955-2219(03)00020-7.
  • Z. Shen et al., Effect of MnO2 on the electrical and dielectric properties of Y-doped Ba0.95Ca0.05Ti0.85Zr0.15O3 ceramics in reducing atmosphere, Ceram. Int. 40 (9), 13833 (2014). DOI: 10.1016/j.ceramint.2014.05.100.

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