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

来源：

THEORETICAL COMPUTER SCIENCE | 2017年 / 697卷

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.

引用

页码：79 / 90

页数：12

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