Unsupervised learning in a fuzzy cognitive map

This paper presents a new model for unsupervised learning and reasoning on a special type of cognitive maps, realized with Petri nets. The unsupervised learning process in the present context adapts the weights of the directed arcs from transition to places in the Petri net. A Hebbian type learning algorithm with a natural decay in weights is employed here to study the dynamic behavior of the algorithm. The algorithm has been found to be conditionally stable for a suitable range of the mortality rate. After convergence of the learning algorithm, the network may be used for determination of the beliefs of the desired propositions embedded in the network from the supplied beliefs of the axioms (places with no input arcs). Because of the conditional stability of the algorithm, it may be used in complex decision-making and learning such as automated car driving in an accident prone environment.