Critical thermal shock temperature prediction of alumina using improved hybrid models based on artificial neural networks and Shannon entropy
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Date
2022-04-26
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Abstract
This study investigates the potential of a simple and Hybrid artificial neural network
(ANN) to predict dense alumina's critical thermal shock temperature (ΔTc). The predictive models
have been constructed using two ANNS models (M1, M2). In the first model (M1), elaboration,
physical and mechanical parameters have been exploited to build three ANNs, namely generalized
linear regression (M1-GLRNN), extreme learning machine (M1-ELM), and radial basis function
(M1-RBFNN). The second model (M2) has been built by the three models mentioned above
incorporated by the Shannon Entropy (SE) method. To compare the performance of all the
developed models, coefficient of correlation (R), root mean square error (RMSE), mean absolute
percentage error (MAPE), and Nash-Sutcliffe efficiency coefficient (NSE) have been considered. It
is found that M2-RBFNN model with (RMSE = 4.3526, MAPE= 0.3406, NSE = 0.9921, and R=
0.9960) had superiority to the M1-RBFNN model (RMSE = 4.7030, MAPE= 0.3003, NSE = 0.9908,
and R = 0.9954). More importantly, the contribution of the present work is that prediction of ΔTc
has been performed through the developed hybrid model (M2-RBFNN), which reduces the number
of inputs from six to only four inputs and offers high accuracy for all the studied variables.