Paper Title :Artificial Neural Network Models for the Prediction of Refractive Index of Pure Alcohols
Author :Kamyar Movagharnezhad, Fateme Kord, Mehrasa Saraie
Article Citation :Kamyar Movagharnezhad ,Fateme Kord ,Mehrasa Saraie ,
(2017 ) " Artificial Neural Network Models for the Prediction of Refractive Index of Pure Alcohols " ,
International Journal of Advances in Science, Engineering and Technology(IJASEAT) ,
pp. 61-64,
Volume-5, Issue-4, Spl. Iss-2
Abstract : In this study, the refractive index of 9 pure samples of 1-alcohols were experimentally investigated at (22 °C and
25 °C). These data were used to establish two different neural network models of multi-layer perceptron (MLP) and radial
basis function (RBF)for prediction of refractive indexes of pure alcohols. For this purpose the temperature, molecular mass,
and functional groups of the compounds were considered as the input parameters and the refractive index was considered as
the only output of the two neural networks. 70% of the data are given as the training data, 15% as the validation data and
15% as the test data. The optimized MLP neural network had the mean square error (MSE) of 0.00000483712 for the
training data, 0.0000649641 for the validation data, 0.000011277 for test data and the correlation coefficient (R) of 0.99238
with 6 optimal neurons in the hidden layer. Also in the optimized RBF neural network with 15 neurons had the minimum
MSE of 0.0000135835.
Keywords - Refractive index, Artificial Neural Network, alcohol, optimization, volumetric percentages
Type : Research paper
Published : Volume-5, Issue-4, Spl. Iss-2
DOIONLINE NO - IJASEAT-IRAJ-DOIONLINE-10512
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Published on 2018-02-12 |
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