✦ LIBER ✦

Using Neural Networks to Predict the Red Electroluminescence of Materials

✍ Scribed by Alana Fernandes Golin, Ricardo Stefani

Book ID: 120728933
Publisher: Springer
Year: 2012
Tongue: English
Weight: 473 KB
Volume: 26
Category: Article
ISSN: 0896-1107
DOI: 10.1007/s10948-012-1690-2

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Quantitative structure-property relation

Quantitative structure-property relationships of electroluminescent materials: Artificial neural networks and support vector machines to predict electroluminescence of organic molecules

✍ GOLIN, ALANA FERNANDES; STEFANI, RICARDO 📂 Article 📅 2013 🏛 Springer-Verlag 🌐 English ⚖ 365 KB

Using neural networks to predict litholo

Using neural networks to predict lithology from well logs

✍ Ford, David A. (author);Kelly, Michael C. (author) 📂 Article 📅 2001 🏛 Society of Exploration Geophysicists ⚖ 51 KB

Using Artificial Neural Networks to Pred

Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect

✍ Mohamed Al-Assadi; Hany A. El Kadi; Ibrahim M. Deiab 📂 Article 📅 2010 🏛 Springer Netherlands 🌐 English ⚖ 526 KB

Predicting the Fatigue Life of Different

Predicting the Fatigue Life of Different Composite Materials Using Artificial Neural Networks

✍ M. Al-Assadi; H. El Kadi; I. M. Deiab 📂 Article 📅 2009 🏛 Springer Netherlands 🌐 English ⚖ 439 KB

The use of neural networks for the predi

The use of neural networks for the prediction of fatigue lives of composite materials

✍ J.A Lee; D.P Almond; B Harris 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 259 KB

Prediction of the functional properties

Prediction of the functional properties of ceramic materials from composition using artificial neural networks

✍ D.J. Scott; P.V. Coveney; J.A. Kilner; J.C.H. Rossiny; N.Mc N. Alford 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 296 KB

We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric mater