Artificial neural network based on hybrid genetic algorithm and prediction of melting points of organic compounds
In order to avoid local minimum points during training BP network,adaptive crossover mutation algorithm and elitist model algorithm were used,which could optimize the weight and bias of BP network so as to build a hyb rid genetic algorithm neural network. The algorithm consisted of the respective advantages of adaptive crossover mutation algorithm and elitist model algorithm . The best solution of problems was obtained by using as few generations as pos sible.In this way, local minimum point was prevented and the better average fitn ess and individual of best fitness were obtained in the algorithm. The artifici al neural network with the above optimization strategy could greatly improve sta bility and speed of convergence and guarantee convergence to a global minimum po int.Application of artificial neural network to predict physical properties of m aterials is a promising field to be developed. In this paper the proposed artificial neural network based on hybrid genetic algorithm was used to predict melting points of organic compounds. The molecular mass, critical density, normal boiling point and dipole moment of orga niccompounds were used in prediction.The predicted values based on artificial neural network with hybrid genetic algorithm were better than the values calculated with the J oback equation and Xu equation.