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Fault Diagnosis Of Ship Power Supply System Based on grey correlation improved BP neural network

机译:基于灰色相关改进的BP神经网络的船舶电源系统故障诊断

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摘要

The ship power system is becoming more and more complex, and the probability of failure is also greatly increased. In this paper, the ship power system fault diagnosis algorithm of BP neural network is put forward based on the grey correlation improved BP neural network, the structure of BP neural network is improved by using grey correlation. In view of the different effects of each hidden layer neuron to the network output layer, the function of the output layer is analyzed by using the gray correlation analysis method, and the gray correlation degree is calculated to effectively eliminate the hidden layer neurons those has small influence on the output layer, which can optimize the structure of BP neural network. The simulation results of fault diagnosis for ship power system are analyzed, which show that the optimized BP neural network can effectively improve the accuracy of the fault diagnosis of the ship power system.
机译:船舶电力系统变得越来越复杂,失败的可能性也大大增加。本文基于灰色相关性改善BP神经网络,提高了BP神经网络的船舶电力系统故障诊断算法,通过使用灰色相关性改善了BP神经网络的结构。鉴于每个隐藏层神经元对网络输出层的不同效果,通过使用灰色相关分析方法分析输出层的功能,并且计算灰色相关度以有效地消除隐藏的层神经元对输出层的影响,可以优化BP神经网络的结构。分析了船舶电力系统故障诊断的仿真结果,表明优化的BP神经网络可以有效提高船舶电力系统故障诊断的准确性。

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