Similarity-Based Perceptual Reasoning for Perceptual Computing

被引：2
|
作者：
Wu, Dongrui [1 ]
Mendel, Jerry M. [1 ]
机构：
[1] Univ South Calif, Inst Signal & Image Proc, Ming Hsieh Dept Elect Eng, 3740 McClintock Ave, Los Angeles, CA 90089 USA
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3 | 2009年
关键词：
Computing with words; perceptual computing; perceptual reasoning; interval type-2 fuzzy sets; rule-based systems; similarity; FUZZY-LOGIC SYSTEMS; SETS;
D O I：
10.1109/FUZZY.2009.5277374
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Perceptual reasoning (PR) is an approximate reasoning method that can be used as a computing with words (CWW) engine in perceptual computing. There can be different approaches to implement PR, e.g., PR using firing intervals is proposed in [8], [9], [16], and similarity-based PR is proposed in this paper. Both approaches satisfy the constraint on a CWW engine, i.e., the result of combining fired rules should lead to a footprint of uncertainty (FOU) that resembles the three kinds of FOUs in a CWW codebook. A comparative study shows that the output FOUs from similarity-based PR more closely resemble the three kinds of FOUs in a codebook, and the resulting linguistic descriptions are more intuitive; so, similarity-based PR is a better choice for a CWW engine.
引用
收藏
页码：700 / +
页数：3
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