International Journal of Man-Machine Studies
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization
Journal of Intelligent Information Systems
Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links
International Journal of Human-Computer Studies
A Learning Algorithm of Fuzzy Cognitive Map in Document Classification
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps
Applied Soft Computing
Genetic learning of fuzzy cognitive maps
Fuzzy Sets and Systems
Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications
Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A causal knowledge-based expert system for planning an Internet-based stock trading system
Expert Systems with Applications: An International Journal
RuleML representation and simulation of Fuzzy Cognitive Maps
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A detailed comparative analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) operating as pattern classifiers, is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM classifier so it equilibrates to a desired state (class mapping). For these purposes, six different types of Hebbian learning algorithms from the literature have been selected and studied in this work. Along with the theoretical description of these algorithms and the analysis of their performance in classifying known patterns, a sensitivity analysis of the applied classification scheme, regarding some configuration parameters have taken place. It is worth noting that the algorithms are studied in a comparative fashion, under common configurations for several benchmark pattern classification datasets, by resulting to useful conclusions about their training capabilities.