基于免疫代理的网络故障诊断研究

针对现有网络故障诊断系统的自适应能力弱、诊断速度慢和故障模式不同等问题,将免疫原理与代理技术相结合,采用分层多步的诊断思想构建诊断模型。基于克隆选择学说,提出新算法,完成检测器的训练。该算法选取了检测器克隆群,引进优化参数,可避免过早收敛和局部最优解的产生,从而改进诊断性能;同时还引入了检测器分类思想,加速诊断过程。与传统故障诊断方法相对比,该模型在处理复杂环境下的网络故障具有较明显的优势。

Research of Network Fault Diagnosis Based on Immune Agents

Aiming at the weak adaptive capacity, slow diagnosis and different fault modes for the existing network fault diagnosis system, combines the immune principle and agent technology and uses hierarchical multiple step diagnosis concept to construct diagnosis model. Based on the clone selection theory, proposes a new algorithm to complete detector training. The algorithm selects the detector colony and introduces dynamic optimizing parameter, which avoiding premature convergence and generation of local optimal solution, greatly improves the diagnostic performance. The introduction of detector classification concept accelerates the diagnosis process. Comparing to the traditional fault diagnosis methods, proves that the model in the treatment of complex environment network fault has obvious advantages.