FCMpy: a python module for constructing and analyzing fuzzy cognitive maps

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 learnin...

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Vydáno v:PeerJ. Computer science Ročník 8; s. e1078
Hlavní autoři: Mkhitaryan, Samvel, Giabbanelli, Philippe, Wozniak, Maciej K, Nápoles, Gonzalo, De Vries, Nanne, Crutzen, Rik
Médium: Journal Article
Jazyk:angličtina
Vydáno: San Diego PeerJ, Inc 23.09.2022
PeerJ Inc
Témata:
Active Hebbian learning
Classification
Cognitive maps
Cognitive models
Digital Libraries
Entropy
FCM
Fuzzy logic
Genetic algorithm
Genetic algorithms
Linguistics
Machine learning
Methods
Modules
Network Science and Online Social Networks
Nonlinear Hebbian learning
Programming Languages
Psychology
Python
Qualitative analysis
Ratings & rankings
Scientific Computing and Simulation
Social Computing
Software
Subject specialists
ISSN:2376-5992, 2376-5992
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Popis
Shrnutí: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.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1078