Radial basis function (RBF) neural network models for the simultaneous estimation of flash point (T
f) and
boiling point (T
b) based on 25 molecular functional groups and their first-order molecular connectivity index
(1χ) have been developed. The success of the whole modeling process depended on a network optimization
strategy based on biharmonic spline interpolation for the selection of an optimum number of RBF neurons
(n) in the hidden layer and their associated spread parameter (σ). The RBF networks were trained by the
Orthogonal Least Squares (OLS) learning algorithm. After dividing the total database of 400 compounds
into training (134), validation (133), and testing (133), the average absolute errors obtained for the validation
and testing sets ranges from 10 °C to 12 °C and 11 °C to14 °C for T
f and T
b, respectively, and are in
agreement with the experimental value of about 10 °C. Results of a standard Partial Least Square (PLS)
regression model for single output predictions range from 23 °C to 24 °C and 18 °C to 20 °C for T
f and T
b,
respectively, indicating the superior predictive ability of the neural model and strongly suggests that a nonlinear
relationship exists between the input and target parameters of the data. The robustness of the neural model
was successfully examined by a random split cross validation based on pooling together of the validation
and test data sets. The study shows that the simple numerical coding of a molecule based on its formula
together with its 1χ is an attractive way of estimating the flammability properties of organic compounds via
an RBF neural network.
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