The proposed hybrid algorithm aims at defining an FCM by the information carried by a large dataset about a specific problem, taking into consideration the BCO and FPO principles. The link between them is represented by the application of the DB-Scan clustering technique allowing to identify the right number of cluster without knowing it a priopri. The hybridisation highlights the efficacy of the algorithm in estimating the correlations among the factors involved for a specific problem, with low RMSE and computational time, demonstrated by the case study example
Fuzzy Cognitive Map (FCM) is an extension of classical cognitive map (CM). It is mainly a soft compu...
ABSTRACT. This work analyses the performance of three different population-based metaheuristic ap-pr...
This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches and algorithms...
The proposed hybrid algorithm aims at defining an FCM by the information carried by a large dataset ...
Abstract⎯In this paper a new hybrid method for training Fuzzy Cognitive Maps is presented. FCMs are ...
The task of classification using intelligent methods and learning algorithms is a difficult task lea...
Clustering task aims at the unsupervised classification of patterns in different groups. To enhance ...
Swarm intelligence that mimic the naturalcollective intelligence to solve the computationalproblem h...
An approach for Fuzzy Cognitive Maps (FCMs) learning, which is based on the minimization of a proper...
This work analyses the performance of three different population-based metaheuristic approaches appl...
Abstract:- This paper presents a hybrid methodology of automatically constructing fuzzy cognitive ma...
This paper presents an extension of a hybrid method for modelling Fuzzy Cognitive Maps (FCMs), which...
Part 8: Intelligent Distributed SystemsInternational audienceFuzzy Cognitive Maps (FCMs) are a frame...
This paper presents a novel approach to improve the accuracy of classification models used for predi...
Fuzzy cognitive maps (FCM) are hybrid tools of artificial neural network and fuzzy logic systems. On...
Fuzzy Cognitive Map (FCM) is an extension of classical cognitive map (CM). It is mainly a soft compu...
ABSTRACT. This work analyses the performance of three different population-based metaheuristic ap-pr...
This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches and algorithms...
The proposed hybrid algorithm aims at defining an FCM by the information carried by a large dataset ...
Abstract⎯In this paper a new hybrid method for training Fuzzy Cognitive Maps is presented. FCMs are ...
The task of classification using intelligent methods and learning algorithms is a difficult task lea...
Clustering task aims at the unsupervised classification of patterns in different groups. To enhance ...
Swarm intelligence that mimic the naturalcollective intelligence to solve the computationalproblem h...
An approach for Fuzzy Cognitive Maps (FCMs) learning, which is based on the minimization of a proper...
This work analyses the performance of three different population-based metaheuristic approaches appl...
Abstract:- This paper presents a hybrid methodology of automatically constructing fuzzy cognitive ma...
This paper presents an extension of a hybrid method for modelling Fuzzy Cognitive Maps (FCMs), which...
Part 8: Intelligent Distributed SystemsInternational audienceFuzzy Cognitive Maps (FCMs) are a frame...
This paper presents a novel approach to improve the accuracy of classification models used for predi...
Fuzzy cognitive maps (FCM) are hybrid tools of artificial neural network and fuzzy logic systems. On...
Fuzzy Cognitive Map (FCM) is an extension of classical cognitive map (CM). It is mainly a soft compu...
ABSTRACT. This work analyses the performance of three different population-based metaheuristic ap-pr...
This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches and algorithms...