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http://hdl.handle.net/1942/29784
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DC Field | Value | Language |
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dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | Salmeron, Jose L. | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2019-10-21T09:35:40Z | - |
dc.date.available | 2019-10-21T09:35:40Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Cybernetics, 51(2), p. 686-695. | - |
dc.identifier.issn | 2168-2267 | - |
dc.identifier.uri | http://hdl.handle.net/1942/29784 | - |
dc.description.abstract | Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in a precise way using crisp numbers. In this paper, we present the long-term grey cognitive networks which expands the recently proposed long-term cognitive networks (LTCNs) with grey numbers. One advantage of our neural system is that it allows embedding knowledge into the network using weights and constricted neurons. In addition, we propose two procedures to construct the network in situations where only historical data are available, and a regularization method that is coupled with a nonsynaptic backpropagation algorithm. The results have shown that our proposal outperforms the LTCN model and other state-of-the-art methods in terms of accuracy. | - |
dc.description.sponsorship | The authors would like to thank the anonymous reviewers for their constructive and insightful remarks. | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.rights | 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. | - |
dc.subject.other | Error backpropagation | - |
dc.subject.other | grey systems | - |
dc.subject.other | neural cognitive modeling | - |
dc.subject.other | recurrent systems | - |
dc.title | Construction and Supervised Learning of Long-Term Grey Cognitive Networks | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 695 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 686 | - |
dc.identifier.volume | 51 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1109/TCYB.2019.2913960 | - |
dc.identifier.pmid | 31107673 | - |
dc.identifier.isi | 000608690900018 | - |
dc.identifier.eissn | 2168-2275 | - |
local.provider.type | Web of Science | - |
local.uhasselt.international | yes | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | Salmeron, Jose L. | - |
item.contributor | VANHOOF, Koen | - |
item.validation | ecoom 2022 | - |
item.fullcitation | NAPOLES RUIZ, Gonzalo; Salmeron, Jose L. & VANHOOF, Koen (2021) Construction and Supervised Learning of Long-Term Grey Cognitive Networks. In: IEEE Transactions on Cybernetics, 51(2), p. 686-695.. | - |
item.accessRights | Restricted Access | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 2168-2267 | - |
crisitem.journal.eissn | 2168-2275 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
manuscript.pdf Restricted Access | Peer-reviewed author version | 4.96 MB | Adobe PDF | View/Open Request a copy |
08718506.pdf Restricted Access | Published version | 1.67 MB | Adobe PDF | View/Open Request a copy |
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