MULTIPLE LINEAR REGRESSION-ARTIFICIAL NEURON NETWORK MIXED MODEL, PREDICTING A LOWER FLAMMABILITY LIMIT TEMPERATURE OF A PURE ORGANIC COMPOUND, BY USING ONLY INFORMATION ABOUT A MOLECULE

摘要

PURPOSE: A MLR(Multiple Linear Regression)-ANN(Artificial Neuron Network) mixed model, predicting a lower flammability limit temperature of a pure organic compound, is provided to obtain the lower flammability limit temperatures of many compounds with high accuracy only through information about a molecule.;CONSTITUTION: A molecule descriptor value about a lower flammability limit temperature of a hydrocarbon series organic compound is prepared. Experimental data is separated into a training set and a test set. An optimum MLRM(Multiple Linear Regression Model) for the training set is explored. The predicted performance of the optimum MLRM is tested on the test set. After an optimum ANNM(Artificial Neural Network Model) divides every samples into three sets, it is explored. The absolute value the difference of a lower flammability limit temperature prediction value, figured out by the MLRM and the ANNM, is compared with an over- suitability preventing standard value. If the difference is greater than the over- suitability preventing standard value, the lower flammability limit temperature prediction value by the MLRM is selected as a lower flammability limit temperature value.;COPYRIGHT KIPO 2012