USING RADIAL BASIS FUNCTION NEURAL NETWORK FOR PREDICTION OF SOLUBILITY OF 1-1 ELECTROLYTES IN NONAQUEOUS SOLVENTS
PDF

Як цитувати

Pushkarova, Y., & Panchenko, V. (2020). USING RADIAL BASIS FUNCTION NEURAL NETWORK FOR PREDICTION OF SOLUBILITY OF 1-1 ELECTROLYTES IN NONAQUEOUS SOLVENTS. Матеріали конференцій МЦНД, 40-41. https://doi.org/10.36074/13.11.2020.v2.05
https://doi.org/10.36074/13.11.2020.v2.05
PDF

Посилання

Sheridan, Q. R., Schneider, W. F., Maginn, E. J. (2016). Anion Dependent Dynamics and Water Solubility Explained by Hydrogen Bonding Interactions in Mixtures of Water and Aprotic Heterocyclic Anion Ionic Liquids. J. Phys. Chem. B, (120, 49), 12679−12686.

Boucher, D., Howell, J. (2016). Solubility Characteristics of PCBM and C-60. J. Phys. Chem. B, (120, 44), 11556−11566.

Hardy, A., Bock, H. (2016). Assessing the Quality of Solvents and Dispersants for Low-Dimensional Materials Using the Corresponding Distances Method. J. Phys. Chem. B, (120, 44), 11607−11617.

Turkson, R. F., Yan, F., Ali, M. K. A., Hu, J. (2016). Artificial neural network applications in the calibration of spark-ignition engines: an overview. JESTEC, (19, 3), 1346−1359.

Marini, F. (2009). Artificial neural networks in food analysis: trends and perspectives. A review. Anal. Chim. Acta, (635), 121–131.

Pushkarova Ya. & Panchenko V. (2020). Prediction of the solubility of 1-1 electrolytes in nonaqueous solvents with the use of the radial basis function artificial neural network. Do desenvolvimento mundial como resultado de realizações em ciência e investigação científica: Coleção de trabalhos científicos «ΛΌГOΣ» com materiais da conferência científico-prática internacional. (Vol. 2, pp. 67−68). 9 de outubro, 2020, Lisboa, Portugal. DOI: https://doi.org/10.36074/09.10.2020.v2.17.

Creative Commons License

Ця робота ліцензується відповідно до Creative Commons Attribution 4.0 International License.

Завантаження

Дані завантаження ще не доступні.

| Переглядів: 6 | Завантажень: 6 |