摘要翻译：
本文分析了目标类型估计的时态/序贯属性数据融合的三种组合规则的行为。比较分析的基础是：Dempster-Shafer理论中提出的Dempster融合规则；比例冲突再分配规则号。5(PCR5)和一种可供选择的类融合规则，将信息融合的组合规则与特定的模糊算子联系起来，重点讨论了基于T范数的合取规则作为普通合取规则的模拟，基于T Conorm的析取规则作为普通析取规则的模拟。研究和估计了TCN融合规则中不同的T-conors和T-范数函数对目标类型估计性能的影响。
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英文标题：
《Tracking object's type changes with fuzzy based fusion rule》
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作者：
Albena Tchamova (IPP BAS), Jean Dezert (ONERA), Florentin Smarandache
  (UNM)
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In this paper the behavior of three combinational rules for temporal/sequential attribute data fusion for target type estimation are analyzed. The comparative analysis is based on: Dempster's fusion rule proposed in Dempster-Shafer Theory; Proportional Conflict Redistribution rule no. 5 (PCR5), proposed in Dezert-Smarandache Theory and one alternative class fusion rule, connecting the combination rules for information fusion with particular fuzzy operators, focusing on the t-norm based Conjunctive rule as an analog of the ordinary conjunctive rule and t-conorm based Disjunctive rule as an analog of the ordinary disjunctive rule. The way how different t-conorms and t-norms functions within TCN fusion rule influence over target type estimation performance is studied and estimated.
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PDF链接：
https://arxiv.org/pdf/0910.1433