Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/30509
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | Jastrzębska, Agnieszka | - |
dc.contributor.author | Mosquera, Carlos | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.contributor.author | Homenda, Władysław | - |
dc.date.accessioned | 2020-02-12T15:11:08Z | - |
dc.date.available | 2020-02-12T15:11:08Z | - |
dc.date.issued | 2020 | - |
dc.date.submitted | 2020-02-10T00:21:37Z | - |
dc.identifier.citation | NEURAL NETWORKS, 124 , p. 258 -268 | - |
dc.identifier.issn | 0893-6080 | - |
dc.identifier.uri | http://hdl.handle.net/1942/30509 | - |
dc.description.abstract | Hybrid artificial intelligence deals with the construction of intelligent systems by relying on both human knowledge and historical data records. In this paper, we approach this problem from a neural perspective, particularly when modeling and simulating dynamic systems. Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are requested to define the interaction among the input neurons. As a second contribution, we introduce a fast and deterministic learning rule to compute the weights among input and output neurons. This parameterless learning method is based on the Moore-Penrose inverse and it can be performed in a single step. In addition, we discuss a model to determine the relevance of weights, which allows us to better understand the system. Last but not least, we introduce two calibration methods to adjust the model after the removal of potentially superfluous weights. | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.rights | © 2020 Elsevier Ltd. All rights reserved. | - |
dc.subject.other | fuzzy cognitive maps | - |
dc.subject.other | hybrid models | - |
dc.subject.other | inverse learning | - |
dc.subject.other | interpretability | - |
dc.title | Deterministic learning of hybrid Fuzzy Cognitive Maps and network reduction approaches | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 268 | - |
dc.identifier.spage | 258 | - |
dc.identifier.volume | 124 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.source.type | Article | - |
dc.identifier.doi | 10.1016/j.neunet.2020.01.019 | - |
dc.identifier.pmid | 32032855 | - |
dc.identifier.isi | WOS:000518860600023 | - |
dc.identifier.eissn | 1879-2782 | - |
local.provider.type | - | |
local.uhasselt.uhpub | yes | - |
item.validation | ecoom 2021 | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | Jastrzębska, Agnieszka | - |
item.contributor | Mosquera, Carlos | - |
item.contributor | VANHOOF, Koen | - |
item.contributor | Homenda, Władysław | - |
item.accessRights | Open Access | - |
item.fullcitation | NAPOLES RUIZ, Gonzalo; Jastrzębska, Agnieszka; Mosquera, Carlos; VANHOOF, Koen & Homenda, Władysław (2020) Deterministic learning of hybrid Fuzzy Cognitive Maps and network reduction approaches. In: NEURAL NETWORKS, 124 , p. 258 -268. | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 0893-6080 | - |
crisitem.journal.eissn | 1879-2782 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
manuscript.pdf | Peer-reviewed author version | 542.36 kB | Adobe PDF | View/Open |
1-s2.0-S0893608020300216-main.pdf Restricted Access | Published version | 744.36 kB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
3
checked on Sep 7, 2020
WEB OF SCIENCETM
Citations
16
checked on Apr 30, 2024
Page view(s)
88
checked on Sep 7, 2022
Download(s)
8
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.