Acta Chimica Sinica ›› 2000, Vol. 58 ›› Issue (10): 1230-1234. Previous Articles     Next Articles

Original Articles

运用模糊神经网络表达和预测链烷烃pVT性质

刘平;程翼宇;刘华   

  1. 浙江大学化学工程系.杭州(310027);浙江工程学院化学系
  • 出版日期:2000-10-15 发布日期:2000-10-15

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

Liu Ping;Cheng Yiyu;Liu Hua   

  1. Zhejiang Univ, Dept Chem Eng.Hangzhou(310027)
  • Online:2000-10-15 Published:2000-10-15

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) allows supervised learning of fuzzy rules from significant examples and is affected unsusceptibly by the problem of local extremes. The network's knowledge 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. According to the training results of FNN, the pVT data of other 14 compounds are predicted. The calculated results are satisfactory. The FNN with the molecular connectivity index is a convenient and effective method to calculate the pVT data.

Key words: ALKANE, THERMODYNAMIC PROPERTIES

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