This paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. The classification performance andinterpretability are of major importance in these systems. In this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). Ourapproach uses a punish...

this paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. the classification performance andinterpretability are of major importance in these systems. in this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). ourapproach uses a punish...

We provide an abstract framework for a Logvinenko-Sereda type theorem, where the classical compactness assumption on support of Fourier transform is replaced by that functions under consideration belong to spectral subspace associated with finite energy interval some lower semibounded self-adjoint operator Euclidean $L^2$-space. Our result then provides bound $L^2$-norm such in terms their thic...