The design of network fault diagnosis system based on PNN

Gao, Yutong; Zhou, Xin

doi:10.1109/icfcc.2010.5497339

Cited by 1 publication

(2 citation statements)

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“…Likewise, PNN maintains excellent characteristics such as high precision of the non-linear algorithm. The corresponding weights values of PNN are the distribution of the model sample, and also the network does not need training, therefore it can meet the requirements of real-time processing [2]. Moreover, its robust characteristic to sample noises, fast speed of its training data and the accuracy rate of the classification, enhance achieved results to have a higher quality [23].…”

Section: Related Workmentioning

confidence: 99%

“…The resultant groups of objects can help a network administrator to take accurate decisions to protect data communications over a network. The method based on back propagation (BP) technique is most extensively used in intelligent diagnosis method of artificial neural network [2]. Statistics show that 80% of neural network models have adopted BP network or its variants.…”

Section: Introductionmentioning

confidence: 99%

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Fault Classification System for Computer Networks Using Fuzzy Probabilistic Neural Network Classifier (FPNNC)

Qader

Adda

2014

Engineering Applications of Neural Networks

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Over the last decade, the world has witnessed the rapid development of networking applications of different kinds, and network domains have become more and more advanced regarding with their level of heterogeneity, complexity and the size. Some obstacles such as availability, flexibility and insufficient scalability have affected the existing centralized network management systems, as networks become more distributed. In this work a Fuzzy Probabilistic Neural Network Classifier (FPNNC) is proposed, comprising a hybrid fault classification algorithm based on Fuzzy Cluster Mean (FCM) with Probabilistic Neural Network (PNN) to classify the detected fault datasets. These results will assist network administrators with a highly effective tool to classify faults that occur in computer network systems, enabling them to take well-informed decisions pertaining to security, faults and performance.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%