A Neuropsychology-inspired Learning System for Human Uncertainty Monitoring

T. Z. Tan, G. Ng, S. Erdogan
{"title":"A Neuropsychology-inspired Learning System for Human Uncertainty Monitoring","authors":"T. Z. Tan, G. Ng, S. Erdogan","doi":"10.1109/ICARCV.2006.345430","DOIUrl":null,"url":null,"abstract":"Uncertainty exists in various complex problems. Yet, human is able to effectively handle these uncertainties and makes appropriate decision. Thus, modeling of human uncertainty process should improve the performance of learning system in uncertain environment. A mechanism for human uncertainty monitoring is the broad and narrow generalization in category learning. This can be modeled using upper and lower membership functions, which corresponds to the broad and narrow generalizations respectively. These upper and lower membership functions can be implemented using the fuzzy rough set (FR) theory. A complementary learning fuzzy neural network (CLFNN) is a functional model of human pattern recognition. It is integrated with the human uncertainty monitoring model and the resultant FRCLFNN offers good classification performance and better representation power as it captures input, linguistic, and rough uncertainties. Experimental result supports that FRCLFNN is a competent decision support system","PeriodicalId":415827,"journal":{"name":"2006 9th International Conference on Control, Automation, Robotics and Vision","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 9th International Conference on Control, Automation, Robotics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2006.345430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Uncertainty exists in various complex problems. Yet, human is able to effectively handle these uncertainties and makes appropriate decision. Thus, modeling of human uncertainty process should improve the performance of learning system in uncertain environment. A mechanism for human uncertainty monitoring is the broad and narrow generalization in category learning. This can be modeled using upper and lower membership functions, which corresponds to the broad and narrow generalizations respectively. These upper and lower membership functions can be implemented using the fuzzy rough set (FR) theory. A complementary learning fuzzy neural network (CLFNN) is a functional model of human pattern recognition. It is integrated with the human uncertainty monitoring model and the resultant FRCLFNN offers good classification performance and better representation power as it captures input, linguistic, and rough uncertainties. Experimental result supports that FRCLFNN is a competent decision support system
人类不确定性监测的神经心理学启发学习系统
不确定性存在于各种复杂问题中。然而,人类能够有效地处理这些不确定性并做出适当的决策。因此,对人的不确定性过程进行建模可以提高学习系统在不确定环境中的性能。人类不确定性监测的一种机制是类别学习中的广义泛化和狭义泛化。这可以使用上隶属函数和下隶属函数来建模,它们分别对应于广义和狭义的泛化。这些上下隶属函数可以用模糊粗糙集(FR)理论来实现。互补学习模糊神经网络(CLFNN)是人类模式识别的一种功能模型。它与人类不确定性监测模型相结合,结果FRCLFNN具有良好的分类性能和更好的表示能力,因为它捕获了输入、语言和粗糙的不确定性。实验结果表明,FRCLFNN是一种有效的决策支持系统
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信