A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems

Single layer feed-forward network (SLFN) is well applied to find mapping relationships between the input data and the output data. However, the SLFN has two obvious shortcomings of the indetermination structure and parameters. Therefore, this paper proposes a novel self-organizing cosine similarity learning network (SO-CSLN), which can obtain a stable structure and suitable parameters. The hidden layer nodes of the SO-CSLN are determined by the rank of the sample covariance matrix based on the central limit theorem. And then the weights are obtained by the entropy theory and the cosine similarity theory. Moreover, compared with the SLFN, the proposed algorithm can overcome the shortcomings of the SLFN and provide better performance with faster convergence and smaller generalization error through different UCI data sets. Finally, the proposed method is applied to building the production prediction model of the ethylene production system in petrochemical industries. The experiment results show that the effectiveness and the practicality of the proposed method. Meanwhile, it can guide ethylene production and improve the energy efficiency.