Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization
Journal of Intelligent Information Systems
Application of fuzzy cognitive maps for cotton yield management in precision farming
Expert Systems with Applications: An International Journal
Combining fuzzy cognitive maps with support vector machines for bladder tumor grading
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Integration of expert knowledge and image analysis techniques for medical diagnosis
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Hi-index | 0.01 |
Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial expert’s beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts’ structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.