AI 中文总结
研究置换无三角形单圈图生成的Cayley图在PMC和MM*模型下的循环可诊断性，证明对于n≥11其循环可诊断性为5n - 10，为评估互连网络诊断能力提供依据。
AI 中文摘要
可诊断性是评估多处理器系统可靠性和自诊断能力的关键参数。作为传统可诊断性的高级扩展，循环可诊断性已被提出，以更全面地评估互连网络的诊断能力。本文证明，在PMC和MM*模型下，对于n≥11，无三角形单圈图生成的Cayley图的循环可诊断性为5n - 10。
英文摘要
Diagnosability is a critical parameter for evaluating the reliability and self-diagnostic capacity of multiprocessor systems. As an advanced extension of traditional diagnosability, cyclic diagnosability has been proposed to enable more comprehensive assessment of the diagnostic capabilities of interconnection networks. In this paper, we prove that under both the PMC and MM* models, the cyclic diagnosability of Cayley graphs generated by triangle-free unicyclic graphs is 5n-10 for n\ge11.
Comments11 pages,8 figures
