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Computer network based on improved neural network fault diagnosis research

机译：基于改进神经网络的计算机网络故障诊断研究

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Computer network is one of the most important equipment in the whole world, with the gradual and rapid development of its scale, howto manage andmaintain the computer network is becomingmore andmore complicated. The network fault diagnosis has become people???s focus. With the development of artificial intelligence, by introducing neural network technology into the area of network fault diagnosis, neural network can bring out its advantages in the fault diagnosis. This article would employ SOMneural network andBP neural network, the sampleswould be clustered by using SOM neural network, and the results of the cluster would be put back into the original samples, whichwould also be setwith certainweights, through the weights??? consistent updating, the convergence speed of BP neural network can be improved. Through using LMalgorithmto improve BP neural network and using computer network diagnosis as practical samples to simulate and analyze computer, the validity of this method has been proved, and at the same time building a system of computer network diagnosis can be very meaningful for theoretical study and practical use.
机译：计算机网络是世界上最重要的设备之一，随着其规模的逐步快速发展，如何管理和维护计算机网络变得越来越复杂。网络故障诊断已成为人们关注的焦点。随着人工智能的发展，通过将神经网络技术引入网络故障诊断领域，神经网络可以发挥其在故障诊断中的优势。本文将使用SOM神经网络和BP神经网络，将样本使用SOM神经网络进行聚类，并将聚类的结果放回原始样本中，并通过权重设置一定的权重？一致的更新，可以提高BP神经网络的收敛速度。通过使用LMalgorithm改进BP神经网络，并以计算机网络诊断为实际样本对计算机进行仿真和分析，证明了该方法的有效性，同时建立计算机网络诊断系统对于理论研究和开发具有重要意义。实际使用。

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