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[23330] Artykuł:

Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm

Czasopismo: Federated Conference on Computer Science and Information Systems (FedCSIS) 2015   Tom: 5, Strony: 547-554
ISSN:  2300-5963
ISBN:  978-8-3608-1066-8
Wydawca:  IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Opublikowano: Wrzesień 2015
Seria wydawnicza:  ACSIS-Annals of Computer Science and Information Systems
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Procent
udziału		 Liczba
punktów
Katarzyna Poczęta orcid logoWEAiIKatedra Systemów Informatycznych *337.50  
Aleksander Iwanowicz Jastriebow orcid logoWEAiIKatedra Systemów Informatycznych *337.50  
Elpiniki I. Papageorgiou33.00  

Grupa MNiSW:  Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science)
Punkty MNiSW: 15
Klasyfikacja Web of Science: Proceedings Paper

Pełny tekstPełny tekst     DOI LogoDOI     Web of Science Logo Web of Science    
Keywords:

Fuzzy Cognitive Maps  Structure Optimization Genetic Algorithm  Real-Coded Genetic Algorithm  Multi-Step Gradient Method 


Abstract:

Fuzzy cognitive map (FCM) is a soft computing methodology that allows to describe the analyzed problem as a set of nodes (concepts) and connections (links) between them. In this paper a new Structure Optimization Genetic Algorithm (SOGA) for FCMs learning is presented for modeling complex decision support systems. The proposed approach allows to automatic construct and optimize the FCM model on the basis of historical multivariate time series. The SOGA defines a new learning error function with an additional penalty for highly complexity of FCM understood as a large number of concepts and a large number of connections between them. The aim of this study is the analysis of usefulness of the Structure Optimization Genetic Algorithm for fuzzy cognitive maps learning. Comparative analysis of the SOGA with other well-known FCM learning algorithms (Real- Coded Genetic Algorithm and Multi-Step Gradient Method) was performed on the example of prediction of rented bikes count. Simulations were done with the ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. The obtained results show that the use of SOGA allows to significantly reduce the structure of the FCM model by selecting the most important concepts and connections between them.


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