TY - JOUR
T1 - FCMpy: a python module for constructing and analyzing fuzzy cognitive maps
AU - Mkhitaryan, S.
AU - Giabbanelli, P.
AU - Wozniak, M.K.
AU - Napoles, G.
AU - De Vries, N.
AU - Crutzen, R.
N1 - Funding Information:
The authors received no funding for this work.
Publisher Copyright:
© Copyright 2022 Mkhitaryan et al.
PY - 2022/9/23
Y1 - 2022/9/23
N2 - FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.
AB - FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.
KW - Active Hebbian learning
KW - FCM
KW - Genetic algorithm
KW - Nonlinear Hebbian learning
KW - Python
KW - ALGORITHM
U2 - 10.7717/peerj-cs.1078
DO - 10.7717/peerj-cs.1078
M3 - Article
C2 - 36262149
SN - 2376-5992
VL - 8
JO - PeerJ Computer Science
JF - PeerJ Computer Science
M1 - e1078
ER -