Yan A, Chen X, Zhang R, Liu M, Hu Z, Fan B T
Department of Chemistry, Lanzhou University, PR China.
SAR QSAR Environ Res. 2000;11(3-4):235-44. doi: 10.1080/10629360008033233.
Artificial Neural Networks (ANNs) with Extended Delta-Bar-Delta (EDBD) back propagation learning algorithm have been developed to predict the standard enthalpy and entropy of 87 acyclic alkanes. Molecular weight, boiling point and density of the compounds were used as input parameters. The network's architecture and parameters were optimized to give maximum performances. The best network was a 3-6-2 ANN, and the optimum learning epoch was about 1320. The results show that the maximum relative errors of enthalpy and entropy are less than 3%. They reveal that the performances of ANNs for predicting the enthalpy and entropy of alkanes are satisfying.
已开发出具有扩展Delta-Bar-Delta（EDBD）反向传播学习算法的人工神经网络（ANN）来预测87种无环烷烃的标准焓和熵。化合物的分子量、沸点和密度被用作输入参数。对网络的架构和参数进行了优化以实现最佳性能。最佳网络是一个3-6-2的人工神经网络，最佳学习轮次约为1320次。结果表明，焓和熵的最大相对误差小于3%。这些结果表明人工神经网络在预测烷烃焓和熵方面的性能令人满意。
