数据融合中模糊子集定理的检验

Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI:10.1109/MFI.1994.398422

D.M. Buede

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引用次数: 4

摘要

在测量和更新数据融合应用中的不确定性时，应该使用哪几种方法，这方面仍然存在很大的分歧。本文研究了两种流行的方法:模糊集和概率论。概率论一直是数据融合应用的传统方法。模糊集在日本和欧洲越来越受欢迎，有许多成功的控制应用。本文简要介绍了模糊集的概念，并将模糊子集定理描述为数据融合的最佳模糊方法。最后比较了子集定理与贝叶斯定理在数据融合中的应用

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Examination of the fuzzy subsethood theorem for data fusion

There continues to be substantial disagreement about which of the several methods to use in measuring and updating uncertainty in data fusion applications. In this paper we examine two popular methods, fuzzy sets and probability theory. Probability theory has been the traditional method for data fusion applications. Fuzzy sets have grown in popularity in Japan and Europe, with many successful control applications. Here we give a simple overview of fuzzy sets and then describe the fuzzy subsethood theorem as the best fuzzy approach for data fusion. Finally we compare the subsethood theorem to Bayes theorem for data fusion applications.<>

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Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems

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