Fuzzy Cognitive Maps for futures studies—A methodological assessment of concepts and methods
Abstract Fuzzy Cognitive Map (FCM) modelling is highly suitable for the demands of future
studies: it uses a mix of qualitative and quantitative approaches, it enables the inclusion of …
studies: it uses a mix of qualitative and quantitative approaches, it enables the inclusion of …
Genetic learning of fuzzy cognitive maps
Fuzzy cognitive maps (FCMs) are a very convenient, simple, and powerful tool for simulation
and analysis of dynamic systems. They were originally developed in 1980 by Kosko, and …
and analysis of dynamic systems. They were originally developed in 1980 by Kosko, and …
Learning fuzzy cognitive maps with modified asexual reproduction optimisation algorithm
This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches
and algorithms. FCMs are fuzzy digraphs with weights and feedback loops, consisting of …
and algorithms. FCMs are fuzzy digraphs with weights and feedback loops, consisting of …
Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform
Fuzzy cognitive maps (FCMs) have been successfully used to model and predict stationary
time series. However, it still remains challenging to deal with large-scale nonstationary time …
time series. However, it still remains challenging to deal with large-scale nonstationary time …
A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps
Z Liu, J Liu - Knowledge-Based Systems, 2020 - Elsevier
Fuzzy cognitive maps (FCMs) have been widely used in time series prediction due to the
excellent performance in dynamic system modeling. However, existing time series prediction …
excellent performance in dynamic system modeling. However, existing time series prediction …
A mutual information-based two-phase memetic algorithm for large-scale fuzzy cognitive map learning
X Zou, J Liu - IEEE Transactions on Fuzzy Systems, 2017 - ieeexplore.ieee.org
Various automatic learning algorithms have been proposed to learn fuzzy cognitive maps
(FCMs), but most of them were only applied to learn small-scale FCMs and the learned …
(FCMs), but most of them were only applied to learn small-scale FCMs and the learned …
Learning of fuzzy cognitive maps using density estimate
Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling
dynamic systems, which are characterized by a great deal of flexibility and adaptability …
dynamic systems, which are characterized by a great deal of flexibility and adaptability …
Towards improved multifactorial particle swarm optimization learning of fuzzy cognitive maps: a case study on air quality prediction
W Liang, Y Zhang, X Liu, H Yin, J Wang, Y Yang - Applied Soft Computing, 2022 - Elsevier
Fuzzy cognitive map (FCM) is a very simple, efficient, and powerful soft computing tool for
modeling and analysis of a complex system. Due to its simplicity and transparency, FCM has …
modeling and analysis of a complex system. Due to its simplicity and transparency, FCM has …
Application of fuzzy cognitive maps to water demand prediction
This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model
and predict time series. The multi-step supervised-learning based-on-gradient methods as …
and predict time series. The multi-step supervised-learning based-on-gradient methods as …
Learning and aggregation of fuzzy cognitive maps-An evolutionary approach
WJ Stach - 2010 - era.library.ualberta.ca
Abstract Fuzzy Cognitive Maps (FCMs) are a widely used, neuro-fuzzy based qualitative
approach for the modeling of dynamic systems, which allow for both static and dynamic …
approach for the modeling of dynamic systems, which allow for both static and dynamic …