Expression and prediction of the pVT properties of linear alkanes using fuzzy neural networks

In this paper, a new fuzzy neural network (FNN) based on genetic algorithms is proposed for studying the pVT properties of Linear alkanes. The method based on fuzzy logic (FL), neural network (NN) and genetic algorithm (GA) a llows supervised learning of fuzzy rules from significant examples and is a ffected unsusceptibly by the problem of local extremes. The network's knowl edge base has a Linguistic representation which makes it easy to understand and interpret. Using this new method and molecular connectivity index, 24 compounds are treated as a training set to extract the fuzzy knowledge base . The knowledge base extracted from examples clearly shows the relationship between the structure of compounds and their physicochemical properties. A ccording to the training results of FNN, the pVT data of other 14 compounds are predicted. The calculated results are satisfactory. The FNN with the m olecular connectivity index is a convenient and effective method to calcula te the pVT data.