In: Knowledge and Information Systems, Volume 4, Issue 1 (2002), pages 112-128. February 2002.
Abstract: When involving evolutionary natural objects, the modeling of dynamic lasses is the main issue for a pattern recognition system. This problem can be avoided by making dynamic the system of pattern recognition which can then enter into various states according to the evolution of the classes. We propose a dynamic recognition system founded on two types of learning. The static aspect of the learning is ensured by classifiers or systems of classifiers, 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.
Keywords: Fuzzy Petri net; Dynamic knowledge representation; Pattern recognition.
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