基于邻域粗糙集自适应调整剪枝门限的免疫网络故障诊断算法

A Fault Diagnosis Algorithm for Immune Network with Self-adaptive Adjustment Pruning Threshold of Neighbor Rough Set

通过在形态空间中建立抗体和抗原的邻域关系,阐述了抗体与抗原的匹配过程,论述了基于免疫网络模型(aiNet)的故障诊断算法中剪枝门限与故障诊断漏诊概率和误诊概率的关系.文中引入粗糙集理论,定义了基于抗体邻域的故障模式边界和故障模式包含关系,给出了自适应调整剪枝门限的观测指标和具体算法.仿真结果表明,本文所提出的故障诊断算法具有优良性能,提高了故障诊断正确率和新故障识别率.

The matching process between the antibody and the antigen is investigated by their near neighbor relationship in the shape space,and the relationship between the pruning threshold and the probabilities of the mismatch and the misdiagnosis is studied for fault diagnosis algorithm based on immune model（aiNet）.The fault model boundaries and the containing relationship of these fault models are defined through the rough set theory,and observation criterion and concrete algorithm for self-adaptive adjustment pruning threshold are presented.The simulation result shows that the fault diagnosis algorithm is excellent,the correct diagnosis probabilities of all the fault types and the rate of new fault detection are improved.