In ethylene cracking process

the changes of feed have many kinds

and due to its expensive feed analyzer

little industrial site equips with it

so online recognition of oil property is important to achieve cracking online optimization.As the traditional fuzzy C-means algorithm is based on the membership of the strike Euclidean distance

the algorithm contains only the mean center

bringing the unity of clustering results.To take full advantage of effective information of cracking feed

this paper proposes a fuzzy membership set method based on hybrid probabilistic model

namely through the establishment of Gaussian mixture model to achieve describing the probability distribution of clustering sample's affiliation

and use EM algorithm to estimate the model parameter's pole maximum likelihood.The algorithm can not only consider mean center of the sample

but also effectively use sample covariance and the weight coefficient information for mode discrimination.Finally

the simulation is based on classic IRIS data clustering and ethylene cracking feedstock identification

verifying the method described in this paper in the index of dunn and Xiebieni is better than fuzzy C-means clustering algorithm

showing that the method is effective.