Attribute coordinate system‐based representation and application method of affective cognition

Abstract The traditional fuzzy Petri nets (FPNs) rarely contain the affective features when making reasoning decisions, primarily because the existing affective computing model is not suitable to be described directly in FPNs. In order to solve this problem, this paper introduces an attribute coordi...

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Main Authors: Ruqi Zhou, Huiyou Chang, Yonghe Lu
Format: Article
Language:English
Published: Wiley 2022-09-01
Series:Electronics Letters
Online Access:https://doi.org/10.1049/ell2.12599
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author Ruqi Zhou
Huiyou Chang
Yonghe Lu
author_facet Ruqi Zhou
Huiyou Chang
Yonghe Lu
author_sort Ruqi Zhou
collection DOAJ
description Abstract The traditional fuzzy Petri nets (FPNs) rarely contain the affective features when making reasoning decisions, primarily because the existing affective computing model is not suitable to be described directly in FPNs. In order to solve this problem, this paper introduces an attribute coordinate system and puts forward the affective representation and computing model based on the extraction and integration relationship between the attributes of things, which describes the affective change curve according to the cognition of the thinking subject to the attributes of the decision object. With this new model of affective computing, it is more convenient to utilize a qualitative mapping model and fuzzy qualitative judgment rules to express affective decisions and conveniently describe affective attributes in FPNs. A reasoning decision case illustrates the proposed method's feasibility and advantages.
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spelling doaj.art-7c8909f770d14a458a5e9205dd25be642023-02-22T06:30:52ZengWileyElectronics Letters0013-51941350-911X2022-09-01582077177310.1049/ell2.12599Attribute coordinate system‐based representation and application method of affective cognitionRuqi Zhou0Huiyou Chang1Yonghe Lu2School of Computer Science and Engineering Sun Yat‐sen University Guangzhou ChinaSchool of Computer Science and Engineering Sun Yat‐sen University Guangzhou ChinaSchool of Information Management Sun Yat‐sen University Guangzhou ChinaAbstract The traditional fuzzy Petri nets (FPNs) rarely contain the affective features when making reasoning decisions, primarily because the existing affective computing model is not suitable to be described directly in FPNs. In order to solve this problem, this paper introduces an attribute coordinate system and puts forward the affective representation and computing model based on the extraction and integration relationship between the attributes of things, which describes the affective change curve according to the cognition of the thinking subject to the attributes of the decision object. With this new model of affective computing, it is more convenient to utilize a qualitative mapping model and fuzzy qualitative judgment rules to express affective decisions and conveniently describe affective attributes in FPNs. A reasoning decision case illustrates the proposed method's feasibility and advantages.https://doi.org/10.1049/ell2.12599
spellingShingle Ruqi Zhou
Huiyou Chang
Yonghe Lu
Attribute coordinate system‐based representation and application method of affective cognition
Electronics Letters
title Attribute coordinate system‐based representation and application method of affective cognition
title_full Attribute coordinate system‐based representation and application method of affective cognition
title_fullStr Attribute coordinate system‐based representation and application method of affective cognition
title_full_unstemmed Attribute coordinate system‐based representation and application method of affective cognition
title_short Attribute coordinate system‐based representation and application method of affective cognition
title_sort attribute coordinate system based representation and application method of affective cognition
url https://doi.org/10.1049/ell2.12599
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AT huiyouchang attributecoordinatesystembasedrepresentationandapplicationmethodofaffectivecognition
AT yonghelu attributecoordinatesystembasedrepresentationandapplicationmethodofaffectivecognition