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一种基于链路结构分析算法和兰斯-威廉姆斯距离的随机方法的新型证据组合方法.
Qi Tang1, Jianyu Xiao1, Kefeng Wu1
1School of Computer Science and Technology, Huaibei Normal University, HuaiBei, AnHui, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种新的证据结合方法,以解决在处理相互矛盾的证据时,Dempster-Shafer (D-S) 规则的局限性. 该方法通过整合SALSA算法和兰斯-威廉姆斯距离来提高DS规则的性能,以获得更强大的数据融合.
科学领域:
- 人工智能的人工智能
- 数据融合数据融合
- 信息理论 信息理论
背景情况:
- 德普斯特-沙弗 (D-S) 组合规则与高度相互矛盾的证据作斗争.
- 现有的方法缺乏有效的策略来管理矛盾的数据输入.
研究的目的:
- 提出一种新的证据结合方法,克服了传统的DS规则的局限性.
- 在存在高冲突的情况下,提高数据融合的准确性和可靠性.
主要方法:
- 开发了一种方法,将链路结构分析 (SALSA) 算法的随机方法与兰斯-威廉姆斯距离结合起来.
- 使用相关系数计算证据冲突并构建一个证据关联网络.
- 使用SALSA和证据支持获得权威权重,然后将兰斯-威廉姆斯距离转换为支持.
- 创建了一个新的校正系数,使用权威权重和证据支持来调整相互矛盾的证据.
- 使用D-S组合规则合纠正证据.
主要成果:
- 提出的方法有效地处理高度相互矛盾的证据,这是传统的DS规则的已知问题.
- 数字结果表明,新的证据组合技术的性能和稳定性得到了提高.
- 整合SALSA和兰斯-威廉姆斯距离可以显著提高证据融合的准确性.
结论:
- 新的证据结合方法成功地解决了传统的DS规则在处理相互矛盾的证据方面的局限性.
- 提出的方法为涉及矛盾信息的数据融合任务提供了更可靠的解决方案.
- 这项研究为提高复杂情景中证据组合的准确性提供了有价值的技术.
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