Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community.
However, despite substantial advances in the theory and applications of FCMs, there is a …

Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based
systems in terms of modeling, decision making, analysis, prediction, classification, etc. This …

Many studies on time series forecasting have employed fuzzy cognitive maps (FCMs).
However, it is required to develop techniques capable of effective responses and great …

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 …

Many real-life situations require ranking alternative decisions with respect to multiple criteria.
The problem becomes more complicated when the knowledge of the considered criteria is …

Landslides are among the many devastating natural calamities that cause damage to life
and property. Predicting landslides is an important task to enable preventive measures to be …

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 …

This study proposes a fuzzy time series forecasting method based on hesitant fuzzy sets for
forecasting in the environment of hesitant information. The proposed method addresses the …

Among various soft computing approaches for time series forecasting, fuzzy cognitive maps
(FCMs) have shown remarkable results as a tool to model and analyze the dynamics of …

Fuzzy cognitive maps have achieved significant success in time series modeling and
forecasting. However, fuzzy cognitive maps still contain weakness to handle the …