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https://olympias.lib.uoi.gr/jspui/handle/123456789/10821Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Papageorgiou, E. I. | en |
| dc.contributor.author | Parsopoulos, K. E. | en |
| dc.contributor.author | Groumpos, P. P. | en |
| dc.contributor.author | Vrahatis, M. N. | en |
| dc.date.accessioned | 2015-11-24T17:00:49Z | - |
| dc.date.available | 2015-11-24T17:00:49Z | - |
| dc.identifier.issn | 0302-9743 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10821 | - |
| dc.rights | Default Licence | - |
| dc.title | Fuzzy cognitive maps learning through swarm intelligence | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2004 | - |
| heal.abstract | A technique for Fuzzy Cognitive Maps learning, which is based on the minimization of a properly defined objective function using the Particle Swarm Optimization algorithm, is presented. The workings of the technique are illustrated on an industrial process control problem. The obtained results support the claim that swarm intelligence algorithms can be a valuable tool for Fuzzy Cognitive Maps learning, alleviating deficiencies of Fuzzy Cognitive Maps, and controlling the system's convergence. | en |
| heal.journalName | Artificial Intelligence and Soft Computing - Icaisc 2004 | 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-2004-Fuzzy Cognitive Maps Learning Through Swarm Intelligence.pdf | 179.24 kB | Adobe PDF | View/Open Request a copy |
This item is licensed under a Creative Commons License
