A fuzzy Petri net for pattern recognition: application to dynamic classes (Q1606570)

When involving evolutionary natural objects, the odeling of dynamic lasses is the main issue for a pattern recognition system. This problem an be avoided by making dynamic the system of pattern recognition which an then enter into various states according to the evolution of the lasses. We propose a dynamic recognition system founded on two types of learning. The static aspect of the learning is ensured by lassifiers or systems of lassifiers, while the dynamic aspect is translated by the learning of the planning of the various states by a fuzzy Petri net. The method is sucessfully applied to a synthetic data set.