Abstract
AbstractFuzzy Cognitive Maps 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 expert’s opinion and the potential convergence to undesired steady states. In order to overcome these deficiencies a possible solution is the utilization of learning methods to adapt the cause-effect relationships of the FCM model. This research work examines the formulation of a weight adaptation method suitable for FCMs based on nonlinear Hebbian-type learning algorithm. A process control problem is presented and its control is investigated using the adaptation technique.KeywordsLearning algorithmsNonlinear Hebbian learningfuzzy logicfuzzy cognitive mapsprocess control
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