Default reasoning and possibility theory
Artificial Intelligence
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Fuzzy Sets and Systems
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Attention to time in fuzzy logic
Fuzzy Sets and Systems
The Optimum Class-Selective Rejection Rule
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust interval regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
On Unifying Probabilistic/Fuzzy and Possibilistic Rejection-Based Classifiers
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
Pattern recognition involves two phases: learning and recognition. Using fuzzy theory, the first phase consists mainly in learning membership functions of classes. The recognition phase (or diagnosis) consists in computing membership degrees of the data to classes and deciding in which class the data fits more appropriately. In real-time processes the system may evolve from one class to another. The main idea of this paper is to propose a method that is able to learn and recognize this evolution. The learning phase is based on fuzzy interval regressions. The second part presents a method that gives a diagnosis depending on the percentage of evolution. A decision method is suggested at the end.