✦ LIBER ✦

Artificial neural networks for prediction of antioxidant capacity of cruciferous sprouts

✍ Scribed by Adam Buciński; Henryk Zieliński; Halina Kozłowska

Book ID: 116916160
Publisher: Elsevier Science
Year: 2004
Tongue: English
Weight: 733 KB
Volume: 15
Category: Article
ISSN: 0924-2244
DOI: 10.1016/j.tifs.2003.09.015

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Prediction of pile bearing capacity usin

Prediction of pile bearing capacity using artificial neural networks

✍ In-Mo Lee; Jeong-Hark Lee 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 694 KB

Application of artificial neural network

Application of artificial neural networks for water quality prediction

✍ A. Najah, A. El-Shafie, O. A. Karim, Amr H. El-Shafie 📂 Article 📅 2012 🏛 Springer-Verlag 🌐 English ⚖ 948 KB

Limitations of artificial neural network

Limitations of artificial neural networks for traffic prediction in broadband networks

✍ Hall, J.; Mars, P. 📂 Article 📅 2000 🏛 The Institution of Electrical Engineers 🌐 English ⚖ 493 KB

APPLICATION OF ARTIFICIAL NEURAL NETWORK

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF VOLTAGE INSTABILITY

✍ VALLABHAN, S. C.; JEYASURYA, B. 📂 Article 📅 1997 🏛 Taylor and Francis Group 🌐 English ⚖ 400 KB

Prediction of flutter derivatives by art

Prediction of flutter derivatives by artificial neural networks

✍ Chern-Hwa Chen; Jong-Cheng Wu; Jow-Hua Chen 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 451 KB

This study presents an approach using artificial neural networks (ANN) algorithm for predicting the flutter derivatives of rectangular section models without wind tunnel tests. Firstly, a database of flutter derivatives is identified from a back-propagation (BP) ANN model that is built using experim

Prediction of Extrudate Properties Using

Prediction of Extrudate Properties Using Artificial Neural Networks

✍ T.J. Shankar; S. Bandyopadhyay 📂 Article 📅 2007 🏛 Institution of Chemical Engineers 🌐 English ⚖ 115 KB