[Application of resilient backpropagation neural network in predicting hydrophobic parameters of alkylbenzenes]

Artificial neural networks have been applied for predicting the hydrophobic parameters of alkylbenzene. Compared with traditional methods it has the advantages of simple operation and wide applications. Based on error back propagation neural networks the relationship among the molecular connectivity index (chi), van der Waals surface area (Aw) and hydrophobic parameter was studied, meanwhile the mathematical model was established and used to predict the hydrophobic parameters. By comparing the hydrophobic parameters of experimental values with those calculated by neural networks, we found they had good agreement. The average relative deviation was less than 1%. Because traditional back propagation network is generally time consuming, resilient backpropagation (RPROP) algorithm was used to solve this problem. By using RPROP algorithm, the hydrophobic parameters were obtained precisely by fast training and simple parameter's selection. It needed less than 1,000 iterations to reach the goal on the computer operated at 1.4 GHz. The present work shows that the artificial neural network is a new powerful tool to predict the physicochemical parameters.