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  failure correlation
The data show that excellent failure correlation is obtained, in both mode I and II loading, through the use of a single parameter, the critical stress intensity.
      
Thus, it is important to construct overlays with disjoint paths, where the failure correlation among logical overlay links is low.
      
Equation 6 reveals the impact of failure correlation on performability.
      


One of the important means of network fault management is to detect and identify the faults by analyzing the performance data of networks. A solution employing neural network (NN) is proposed in this paper. This solution is developed for the fault identification in the large-scale networks,and it detects and identifies faults through analyzing statistical characteristics of numerical data associated with the network performance. In order to improve the system performance, the software engineering concept of...

One of the important means of network fault management is to detect and identify the faults by analyzing the performance data of networks. A solution employing neural network (NN) is proposed in this paper. This solution is developed for the fault identification in the large-scale networks,and it detects and identifies faults through analyzing statistical characteristics of numerical data associated with the network performance. In order to improve the system performance, the software engineering concept of diversity is introduced. NNs are subsequent to training, and are combined by means of a majority voter to form a system. At the same time, the concept of failure correlation coefficient between NNs is defined, and the NNs with negative failure correlation coefficient are selected according to the results of computer simulation, with which the system has achieved 100% generalization to the test sets.

通过分析网络性能数据来发现和识别故障是网络故障管理的重要手段之一。本文研究并提出了一种基于神经网络的故障识别方案,通过分析网络性能的数字特征来识别大型网络中的故障。为了提高系统的识别率,引入了软件工程中多版本编程技术,并行训练多组神经网络,由多数决投票机制合成为一个系统。同时定义了神经网络之间的错误关联系数概念,依据计算机仿真的结果,选取错误负相关的神经网络所组成的系统,对测试集能够达到100%的识别率。

 
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