【摘要】 对光纤网络断点进行准确检测,对维护网络安全是十分重要的。大规模光纤通信网络节点分布的分散,产生的断点存在随机性,单个断点也不会直接影响网络的通信。传统依据自主感应分析方法的断点检测模型在网络结构下,无法通过节点的通断来判断断点,需要逐个遍历网络节点进行关联,存在检测效率过低的问题。提出了一种依据免疫识别的神经网络光纤通信网络断点检测模型,按照免疫识别原理塑造神经网络检测器,通过训练将大型光纤通信网络的故障模式信息存储在分布检测器中,检测器用于采集光线通信网络的异常模式特征,当检测时应同特征样本匹配时则激活该检测器,按照检测器的激活状态检测大型光纤通信网络的断点,并给出了相应的训练方法。仿真结果表明,所提模型可准确检测出大型光纤通信网络断点,并且具有较高的检测效率。更多还原

【Abstract】 A breakpoint detection model for optical fiber communication networks is presented based on immune recognition neural network. According to the principle of immune recognition neural network detector,through training the failure mode of large optical fiber communication network information stored in a distributed detector,the detector is used to collect light anomaly pattern characteristics of communication network. When the test samples of the same features matches,the detector is activated. According to the activated state of the detector,the breakpoint of large optical fiber communication network is detected,and the corresponding training methods are given. The result of simulation experiment shows that the proposed model can accurately detect the breakpoint of large optical fiber communication network,and has high detection efficiency.更多还原