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• W2062676319 abstract "Artificial neural networks (ANNs) were successfully developed for the modeling and prediction dielectric constant of different ternary liquid mixtures at various temperatures (−10°C ≤ t ≤ 80°C) and over the complete composition range (0 ≤ x 1, x 2, x 3 ≤ 1). A three-layered feed forward ANN with architecture 7-16-1 was generated using seven parameters as inputs and its output is dielectric constant of media. It was found that properly selected and trained neural network could fairly represent the dependence of dielectric constant of different ternary liquid mixtures on temperature and composition. For the evaluation of the predictive power of the generated ANN, an optimized network was applied for predicting the dielectric constant in the prediction set, which were not used in the modeling procedure. Squared correlation coefficient (R 2) and root mean square error for prediction set are 0.9997 and 0.2060, respectively. The mean percent deviation (MPD) for the property in the prediction set is 0.8892%. The results show nonlinear dependence of dielectric constant of ternary mixed solvent systems on temperature and composition is significant." @default.
• W2062676319 created "2016-06-24" @default.
• W2062676319 creator A5028558812 @default.
• W2062676319 date "2007-08-01" @default.
• W2062676319 modified "2023-10-17" @default.
• W2062676319 title "Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks" @default.
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• W2062676319 doi "https://doi.org/10.1080/00319100601089679" @default.
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