پديدآورندگان :
Vafaei Niousha vafaei.niousha@gmail.com Faculty of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran; , MovagharNejad Kamyar Faculty of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
چكيده فارسي :
In this study, an artificial neural network
was used to predict the refractive index of the binary
solutions, including alcohol-alkane, alkane-alkane
and alcohol-alcohol. Inputs of the neural network are
molecular mass of the first substance, second
substance molecular weight, temperature, mole
fraction of the first substance, functional groups, and
the output of neural network is refractive index of
binary solutions. Optimal design for a neural
network, is a network with the error back
propagation algorithm with Levenberg Marquardt
training function and tangent hyperbolic transfer
function for hidden layer and linear transfer function
for the output layer. Neural network modeled has 8
inputs, 10 neurons in the hidden layer, and one
neuron in the output layer. Then, we optimized the
neural network first using genetic algorithm, after
that using particle swarm optimization algorithm.
The results show that the f the refractive index can
be predicted using ANN-GA model with a 2.4%
error and using ANN-PSO model, with error of
2.235%. The results show that ANN-PSO model has
higher ability to predict and interpolation refractive
index of binary solutions data, including alcoholalkane,
alkane-alkane and alcohol-alcohol compared
to the ANN-GA model.