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On the modeling of neural cognition for social network applications
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.
KTH, School of Architecture and the Built Environment (ABE).
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2017 (English)In: 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017), IEEE , 2017, p. 1569-1574Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we study neural cognition in social network. A stochastic model is introduced and shown to incorporate two well-known models in Pavlovian conditioning and social networks as special case, namely Rescorla-Wagner model and Friedkin-Johnsen model. The interpretation and comparison of these model are discussed. We consider two cases when the disturbance is independent identically distributed for all time and when the distribution of the random variable evolves according to a markov chain. We show that the systems for both cases are mean square stable and the expectation of the states converges to consensus.

Place, publisher, year, edition, pages
IEEE , 2017. p. 1569-1574
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-228177ISI: 000426981500250OAI: oai:DiVA.org:kth-228177DiVA, id: diva2:1209005
Conference
2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS
Note

QC 20180521

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2018-05-21Bibliographically approved

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http://ccta2017.ieeecss.org/

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Wei, JieqiangWu, JunfengMolinari, MarcoCvetkovic, VladimirJohansson, Karl H.

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Wei, JieqiangWu, JunfengMolinari, MarcoCvetkovic, VladimirJohansson, Karl H.
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ACCESS Linnaeus CentreEnergy TechnologySchool of Architecture and the Built Environment (ABE)
Probability Theory and Statistics

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CiteExportLink to record
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Citation style
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More styles
Language
  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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