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Research on Fault Diagnosis of Civil Aircraft Based on AFPN

机译:基于AFPN的民航飞机故障诊断研究

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As the knowledge in the fault diagnosis of civil aircraft is dynamic and uncertain, a method based on the Adaptive Fuzzy Petri Net(APFN) is proposed to solve the problem. AFPN not only takes the descriptive advantages of fuzzy Petri net, but also has learning ability like neural network. By this mean, firstly set up a fuzzy Petri net using the fuzzy production rule. Then the parameters of the fuzzy Petri net are trained by BP learning algorithm. At last, when the weights of the fuzzy Petri net are fixed, the fault origin can be found through the fault inference. At the end of the paper, an experiment is designed to demonstrate that the approach is feasible and effective in fuzzy reasoning.
机译:针对民用飞机故障诊断知识的动态性和不确定性,提出了一种基于自适应模糊Petri网的方法。 AFPN不仅具有模糊Petri网的描述优势,而且具有神经网络等学习能力。通过这种方式,首先使用模糊生产规则建立了一个模糊Petri网。然后通过BP学习算法训练模糊Petri网的参数。最后,当模糊Petri网的权重固定时,可以通过故障推理找到故障源。在本文的最后,设计了一个实验来证明该方法在模糊推理中是可行和有效的。

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