Abstract
A detailed analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM so the model equilibrates to a desired state. For this manner, four different types of Hebbian learning algorithms have been proposed in the past. Along with the theoretical description of these algorithms, their performance in system modeling problems is investigated in this work. The algorithms are studied in a comparative fashion by using appropriate performance indices and useful conclusions about their training capabilities are experimentally derived.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Fuzzy Systems |
| Pages | 851-858 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan Duration: Jun 27 2011 → Jun 30 2011 |
Other
| Other | 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 |
|---|---|
| Country | Taiwan |
| City | Taipei |
| Period | 6/27/11 → 6/30/11 |
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Keywords
- fuzzy cognitive maps
- hebbian learning
- system modeling
- training
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Applied Mathematics
- Theoretical Computer Science
Cite this
Training Fuzzy Cognitive Maps by using Hebbian learning algorithms : A comparative study. / Papakostas, G. A.; Polydoros, A. S.; Koulouriotis, D. E.; Tourassis, V. D.
IEEE International Conference on Fuzzy Systems. 2011. p. 851-858 6007544.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Training Fuzzy Cognitive Maps by using Hebbian learning algorithms
T2 - A comparative study
AU - Papakostas, G. A.
AU - Polydoros, A. S.
AU - Koulouriotis, D. E.
AU - Tourassis, V. D.
PY - 2011
Y1 - 2011
N2 - A detailed analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM so the model equilibrates to a desired state. For this manner, four different types of Hebbian learning algorithms have been proposed in the past. Along with the theoretical description of these algorithms, their performance in system modeling problems is investigated in this work. The algorithms are studied in a comparative fashion by using appropriate performance indices and useful conclusions about their training capabilities are experimentally derived.
AB - A detailed analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM so the model equilibrates to a desired state. For this manner, four different types of Hebbian learning algorithms have been proposed in the past. Along with the theoretical description of these algorithms, their performance in system modeling problems is investigated in this work. The algorithms are studied in a comparative fashion by using appropriate performance indices and useful conclusions about their training capabilities are experimentally derived.
KW - fuzzy cognitive maps
KW - hebbian learning
KW - system modeling
KW - training
UR - http://www.scopus.com/inward/record.url?scp=80053057168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053057168&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2011.6007544
DO - 10.1109/FUZZY.2011.6007544
M3 - Conference contribution
AN - SCOPUS:80053057168
SN - 9781424473175
SP - 851
EP - 858
BT - IEEE International Conference on Fuzzy Systems
ER -