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The Application of Genetic BP Neural Network and D-S Evidence Theory in the Complex System Fault Diagnosis

机译:遗传BP神经网络和D-S证据理论在复杂系统故障诊断中的应用

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According to such defects as that BP neural network is easy to fall into the local minimum values and is sensitive to the initial values of weights and thresholds, genetic algorithm was combined with BP neural network to improve the initial values of weights and thresholds. According to the fault diagnosis of complex system, based on cognitive science, its structure was decomposed; the fault diagnostic sub-model using genetic BP neural network was established to the different parts of the structure; D-S evidence theory was used to different sub-models for the global information fusion. GNN-DS diagnostic model was established for the fault diagnosis of complex system, this model reduced the uncertainty of fault diagnosis of complex system.
机译:针对BP神经网络容易陷入局部最小值,对权重和阈值的初始值敏感的缺陷,将遗传算法与BP神经网络相结合,提高了权重和阈值的初始值。根据复杂系统的故障诊断,以认知科学为基础,对其结构进行分解;利用遗传BP神经网络对结构的不同部分建立了故障诊断子模型。 D-S证据理论被用于全球信息融合的不同子模型。建立了复杂系统故障诊断的GNN-DS诊断模型,降低了复杂系统故障诊断的不确定性。

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