Hard and Soft Information Fusion Using Measures
We are interested in the problem of multi-source uncertain information fusion in the case when the information provided can be both soft and hard information 1. We note that hard sensor provided information generally has a probabilistic type of uncertainty whereas soft linguistic information typically introduces a possibilistic type of uncertainty 2. In order to provide a unified framework for the representation of these different types of uncertain information we use a set measure approach for the representation of uncertain information. We discuss a set measure representation of uncertain information. In the multi-source fusion problem, in addition to having a collection of pieces of information that must be fused, we need to have some expert provided instructions on how to fuse these pieces of information. Generally these instructions can involve a combination of linguistically and mathematically expressed directions. In the course of this work we begin to consider the fundamental task of how to translate these instructions into formal operations that can be applied to our information. This requires us to investigate the important problem of the aggregation of set measures, Ease of Use.
Ronald R.Yager
Machine Intelligence Institute Iona College New Rochelle, NY 10801
国际会议
The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)
杭州
英文
13-16
2010-11-15（万方平台首次上网日期，不代表论文的发表时间）