Representing and analyzing the complexity of models constructed by data is a difficult and
challenging task, hence the need for new, more effective techniques emerges, despite the …

Fuzzy cognitive maps (FCMs) is a knowledge representation tool that can be exploited for
predicting multivariate time-series. FCM model represents dependencies among data …

In this paper, we introduce a novel approach to time-series prediction realized both at the
linguistic and numerical level. It exploits fuzzy cognitive maps (FCMs) along with a recently …

This paper proposes a two-stage prediction model, for multivariate time series prediction
based on the efficient capabilities of evolutionary fuzzy cognitive maps (FCMs) enhanced by …

This paper introduced a new ensemble learning approach, based on evolutionary fuzzy
cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM …

In this paper we propose a new approach to learning fuzzy cognitive maps (FCMs) as a
predictive model for time series forecasting. The first contribution of this paper is the dynamic …

In this study, we propose a new hybrid approach for time series prediction based on the
efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms …

Air pollution is related to the concentration of harmful substances in the lower layers of the
atmosphere and it is one of the most serious problems threatening the modern way of life …

Fuzzy cognitive maps (FCMs) represent the decision process in the form of a graph that is
usually easy to interpret and, therefore, can be applied as a convenient decision-support …

Abstract Development of Fuzzy Cognitive Maps (FCMs) that accurately describe a given
dynamic system is a challenging task which in many cases cannot be fully completed based …