An extended power geometric technique for multiple-attribute decision-making under single-valued neutrosophic sets and applications to embedded computers’ performance evaluation

被引:0
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作者
Li Y. [1 ]
Zhang M. [2 ]
机构
[1] Jining Medical University, Shandong, Jining
[2] Jining Yucai High School, Shandong, Jining
关键词
Embedded computers’ performance evaluation; Generalized geometric Bonferroni mean (GGBM) operator; Multiple attribute decision making (MADM); Single-valued neutrosophic sets (SVNSs);
D O I
10.1007/s00500-024-09781-1
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摘要
Embedded computer systems refer to specialized computer systems that integrate operating systems and functional software into computer hardware systems, are application centric, based on computer technology, and have tailorable software and hardware. They have strict requirements for real-time performance, reliability, cost, volume, and power consumption. The performance and security evaluation of embedded systems is an important aspect of the development, development, and integration process of embedded systems, which can provide various quantitative analysis basis for the development and technical transformation of embedded products. The performance and security evaluation of embedded systems is a very complex issue, and evaluators may have different understandings of the problem due to their different starting points and observation perspectives. The embedded computers’ performance evaluation is a classical multiple-attribute decision-making (MADM) issue. In such paper, the generalized weighted geometric Bonferroni mean (GWGBM) operator is built for MADM with single-valued neutrosophic sets (SVNSs). Then, the single-valued neutrosophic number generalized power geometric BM (SVNNGPGBM) operator is built and then the MADM decision methods are proposed based on the SVNNGPGBM operator and power geometric (PG) operator. Finally, an example about embedded computers’ performance evaluation and some comparative analysis were given to demonstrate the SVNNGPGBM method. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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页码:10301 / 10316
页数:15
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