Abstract
More and more enterprises hope to achieve cooperation and win–win. However, many companies often have problems such as insufficient partner credit, which seriously affects the quality of cooperation. In order to effectively evaluate the credit, this paper constructs a personal credit evaluation model. The model compares the weight adjustment method with BP neural network and other methods. Compared with the BP neural network weight adjustment algorithm, the improved algorithm has obvious advantages in accuracy and convergence speed. The simulation results show that the green supply chain cooperation credit evaluation model can better evaluate the environmental behavior of enterprises. The BP neural network can better solve the problem of slow convergence and premature convergence, and can search data more accurately. The algorithm has good robustness. The evaluation model has high optimization accuracy, which shows that BP neural network can better learn and evaluate the credit of green supply chain at different levels.
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Acknowledgements
This work was supported by the Major Program of National Social Science Foundation of China (19ZDA082).
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Chen, J., Huang, S. Evaluation model of green supply chain cooperation credit based on BP neural network. Neural Comput & Applic 33, 1007–1015 (2021). https://doi.org/10.1007/s00521-020-05420-6
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DOI: https://doi.org/10.1007/s00521-020-05420-6