The InCognitive App provides a GUI and backend code for Fuzzy Cognitive Map (FCM) simulations.
-
Deployment and graphical representation of a FCMs.
-
Selection of FCM-transfer-function's parameter lambda; it is based on the methodology of [1]. The yielded lambda value guarantees that the stand-alone FCM or FCM-MC simulations (see next functionalities) do not yield chaotic or indefinite oucomes.
-
Stand-alone-FCM simulation, based on [1] and [2]. It provides the final nodes' state vector, given predifined input-node values and FCM parameters (e.g. FCM-transfer-function's parameter lambda).
-
A combination of FCM and Monte Carlo (MC) simulation. This combination, FCM-MC, analyses the uncertainty propagation from input nodes all the way to the output nodes, in case the "weights" of FCM edges and/or the input node values are random variables. It's outcome is the final node value distributions, given that some, or all, of the input nodes and/or the FCM parameters (e.g. weights) are random variables.
To run the application, the user needs only to execute the main.py module. The GUI provides all necessary interaction between the end-user and backend-code. For further details, see the manual here: https://github.com/ThemisKoutsellis/InCognitive/wiki/Manual
[1] T. Koutsellis et al., "Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps," 2022, Oper Res Int J, 22, pp. 5733–5763, https://doi.org/10.1007/s12351-022-00717-x
[2] T. Koutsellis et al., "Normalising the Output of Fuzzy Cognitive Maps," 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA), 2022, pp. 1-7, https://doi.org/10.1109/IISA56318.2022.9904369.
[3] Koutsellis, T., Xexakis, G., Koasidis, K., Frilingou, N., Karamaneas, A., Nikas, A., & Doukas, H. (2023). In-Cognitive: A web-based Python application for fuzzy cognitive map design, simulation, and uncertainty analysis based on the Monte Carlo method. SoftwareX, 23, 101513. https://doi.org/10.1016/j.softx.2023.101513