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https://olympias.lib.uoi.gr/jspui/handle/123456789/10866Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Papageorgiou, E. I. | en |
| dc.contributor.author | Parsopoulos, K. E. | en |
| dc.contributor.author | Stylios, C. | en |
| dc.contributor.author | Groumpos, P. P. | en |
| dc.contributor.author | Vrahatis, M. N. | en |
| dc.date.accessioned | 2015-11-24T17:01:04Z | - |
| dc.date.available | 2015-11-24T17:01:04Z | - |
| dc.identifier.issn | 0925-9902 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10866 | - |
| dc.rights | Default Licence | - |
| dc.subject | fuzzy cognitive maps | en |
| dc.subject | particle swarm optimization | en |
| dc.subject | swarm intelligence | en |
| dc.subject | soft computing | en |
| dc.subject | supervisory control-systems | en |
| dc.subject | evolutionary computation | en |
| dc.subject | convergence | en |
| dc.subject | algorithm | en |
| dc.subject | challenge | en |
| dc.subject | selection | en |
| dc.subject | design | en |
| dc.title | Fuzzy cognitive maps learning using particle swarm optimization | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1007/s10844-005-0864-9 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2005 | - |
| heal.abstract | This paper introduces a new learning algorithm for Fuzzy Cognitive Maps, which is based on the application of a swarm intelligence algorithm, namely Particle Swarm Optimization. The proposed approach is applied to detect weight matrices that lead the Fuzzy Cognitive Map to desired steady states, thereby refining the initial weight approximation provided by the experts. This is performed through the minimization of a properly defined objective function. This novel method overcomes some deficiencies of other learning algorithms and, thus, improves the efficiency and robustness of Fuzzy Cognitive Maps. The operation of the new method is illustrated on an industrial process control problem, and the obtained simulation results support the claim that it is robust and efficient. | en |
| heal.journalName | Journal of Intelligent Information Systems | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| parsopoulos-2005-Fuzzy Cognitive Maps Learning Using Particle.pdf | 216.32 kB | Adobe PDF | View/Open Request a copy |
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