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On g-good-neighbor conditional diagnosability of (n, k)-star networks
被引:18
|作者:
Wei, Yulong
[1
]
Xu, Min
[1
]
机构:
[1] Beijing Normal Univ, Sch Math Sci, Lab Math & Complex Syst, Minist Educ, Beijing 100875, Peoples R China
基金:
中国国家自然科学基金;
关键词:
PMC model;
MM* model;
(n;
k)-Star networks;
Fault diagnosability;
MATCHING COMPOSITION NETWORKS;
COMPARISON DIAGNOSIS MODEL;
MM-ASTERISK MODEL;
PMC MODEL;
INTERCONNECTION NETWORKS;
MULTIPROCESSOR SYSTEMS;
FAULT-TOLERANCE;
STAR GRAPHS;
HYPERCUBES;
D O I:
10.1016/j.tcs.2017.07.031
中图分类号:
TP301 [理论、方法];
学科分类号:
081202 ;
摘要:
The g-good-neighbor conditional diagnosability is a new measure for fault diagnosis of systems. Xu et al. (2017) [27] determined the g-good-neighbor conditional diagnosability of (n, k)-star networks S-n,S-k (i.e., t(g)(S-n,S-k)) with 1 <= k <= n-1 for 1 <= g <= n -k under the PMC model and the MM* model. In this paper, we determine t(g)(S-n,S-k) for all the remaining cases with 1 <= k <= n-1 for 1 <= g <= n-1 under the two models, from which we can obtain the g-good-neighbor conditional diagnosability of the star graph obtained by Li et al. (2017) [16] for 1 <= g <= n- 2. (C) 2017 Elsevier B.V. All rights reserved.
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页码:79 / 90
页数:12
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