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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 structure optimization algorithms and artificial neural networks (ANNs). In the first-stage, an evolutionary FCM is constructed automatically from historical time series data using the previously proposed structure optimization...
Fuzzy cognitive map (FCM) allows to discover knowledge in the form of concepts significant for the analyzed problem and causal connections between them. The FCM model can be developed by experts or using learning algorithms and available data. The main aspect of building of the FCM model is concepts selection. It is usually based on the expert knowledge. The aim of this paper is to develop and analyze...
Fuzzy cognitive map (FCM) is a simple and user friendly tool for modeling complex systems. It is described by the set of the concepts and the connections between them. FCM can be initialized based on expert knowledge or automatic constructed with the use of learning algorithms. Most learning methods focus on data error and the structure of the resulting model significantly differs from the reference...
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 and artificial neural networks (ANNs). The proposed structure optimization genetic algorithm (SOGA) for automatic construction of FCM is used for modeling complexity based on historical time series, and artificial neural...
Fuzzy cognitive map (FCM) is a soft computing methodology that allows to describe the analyzed problem as a set of nodes (concepts) and connections (links) between them. In this paper a new Structure Optimization Genetic Algorithm (SOGA) for FCMs learning is presented for modeling complex decision support systems. The proposed approach allows to automatic construct and optimize the FCM model on the...
The purposes of this research are to find a model to forecast the electricity consumption in a household based on fuzzy cognitive map (FCM) prediction capabilities. The data analysis has been performed with three different learning algorithms based on the fuzzy cognitive map model which are (a) the multi-step gradient method (MGM), (b) the real coded genetic algorithm (RCGA) and (c) the structure...
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 well as population-based learning, with the use of real coded genetic algorithms, are described. In this study, a new structure optimization genetic algorithm for fuzzy cognitive maps learning is proposed for automatic construction...
In the paper some analysis of multi-step learning algorithms for fuzzy cognitive map (FCM) is given. FCMs, multi-step supervised learning based on gradient method and unsupervised one based on nonlinear Hebbian learning (NHL) algorithm are described. Comparative analysis of these methods to one-step algorithms, from the point of view of the speed of convergence of learning algorithm and the influence...
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