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Intelligent assessment method of marine engine-room operation based on deep belief networks
DUAN Zun-lei，REN Guang，Li Ye
Journal of Dalian Maritime University ›› 2017, Vol. 43 ›› Issue (3) : 89-94.
PDF(979 KB)
PDF(979 KB)
Intelligent assessment method of marine engine-room operation based on deep belief networks
The intelligent assessment method for marine engine-room operation based on deep belief networks was proposed according to the task of promoting the electronic and intelligent assessment for seafarers in China. According to the characteristics of assessment for practical engine-room operation, the method for determining the hierarchical network structure was provided. Based on the large amounts of extracting practical operation data as the training data, the restricted Boltzmann machines were trained by greedy training algorithm. Finally, the assessment model was generated by using BP algorithm for network fine-tuning. In the simulation experiments, the prediction results of the deep auto-encoder network, BP neural network and the proposed model were verified comparably. Results show that the proposed model is objective and impartial, and the error is the minimum, and the problem that multilayer neural networks fall into local optimum is avoided.
deep belief networks / engine-room / intelligent assessment / contrastive divergence algorithm
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