The aim of this study was to correlate the results of experimental data using DTA method and predictions of artificial neural
network (ANN) and multivariate linear regression (MLR). Thermal decomposition of polymers was analyzed by simultaneous DTA
method, and kinetic parameters (critical points, the change of enthalpy and entropy) of polymers were investigated. A computer
model based on multilayer feed forwarding back propagation and multilayer linear regression model were used for the prediction
of critical points, phase transitions of low-density polyethylene (LDPE) and mid-density polyethylene. As a result of our
study, we concluded that ANN model is more suitable than MLR about prediction of experimental data.
Authors:Aidin Pahlavan, Mohammad Hassan Kamani, Amir Hossein Elhamirad, Zahra Sheikholeslami, Mohammad Armin, and Hanieh Amani
purpose, MultivariateLinearRegression (MLR) in the form of step-wise algorithm was applied to assess the relationship among the quality flour and dough and the final breads. By entering or extracting each variable in the model, the most influential