Rule-based reasoning as Boolean transformations
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Petri nets for rule-based decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
A multiple classifier system using ambiguity rejection for clustering-classification cooperation
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - special issue on measures and aggregation: formal aspects and applications to clustering and decision
Knowledge Representation Using Fuzzy Petri Nets
IEEE Transactions on Knowledge and Data Engineering
Temporal knowledge representation and reasoning techniques usingtime Petri nets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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When involving evolutionary natural objects, the modeling of dynamic classes 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 successfully applied to a synthetic data set.