A Compromise-Based New Approach to Learning Fuzzy Cognitive Maps

Miraç Murat*, Umut Asan

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

3 Atıf (Scopus)

Özet

Fuzzy Cognitive Maps (FCMs), first introduced by Kosko, are graph-based knowledge representation tools. In order to improve the efficiency, robustness and accuracy of FCMs, different learning approaches and algorithms have been introduced in the literature. The algorithms aim to revise the initial knowledge of experts and/or extract useful knowledge from historical records in order to yield learned weights. One considerable drawback of FCM is that, in its original form, it often yields the same output under different initial conditions. Since the results of the learning algorithms are highly dependent on the reasoning mechanism (i.e. updating function) of FCMs, this drawback also affects the performance and accuracy of these algorithms. Therefore, problems including (conflicting) multiple initial vectors, multiple weight matrices and multiple desired final state vectors have received only limited attention. In order to address this issue and provide a better modeling framework for this type of problems, a compromise-based new fuzzy cognitive mapping approach based on particle swarm optimization is suggested. To justify the effectiveness and applicability of the proposed approach, an illustrative example is provided.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıIntelligent and Fuzzy Techniques
Ana bilgisayar yayını alt yazısıSmart and Innovative Solutions - Proceedings of the INFUS 2020 Conference
EditörlerCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
YayınlayanSpringer
Sayfalar1172-1180
Sayfa sayısı9
ISBN (Basılı)9783030511555
DOI'lar
Yayın durumuYayınlandı - 2021
EtkinlikInternational Conference on Intelligent and Fuzzy Systems, INFUS 2020 - Istanbul, Turkey
Süre: 21 Tem 202023 Tem 2020

Yayın serisi

AdıAdvances in Intelligent Systems and Computing
Hacim1197 AISC
ISSN (Basılı)2194-5357
ISSN (Elektronik)2194-5365

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???event.eventtypes.event.conference???International Conference on Intelligent and Fuzzy Systems, INFUS 2020
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot21/07/2023/07/20

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Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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