This paper concerns the concept of set-membership identifiability introduced in [4]. Given a model, a set-membership identifiable set is a connected set in the parameter domain of the model such that its corresponding trajectories are distinct to trajectories arising from its complementary. For obtaining the so-called set-membership identifiable sets, we propose an algorithm based on interval a...

In the present paper, a Nonlinear Set Membership prediction method previously proposed by the authors is applied to a river flow prediction problem. The method does not require the choice of the functional form of the model used for prediction, but assumes a bound on the gradient norm of the regression function defining the model. The method is used for the univariate prediction of the time ser...

The seminal work on set membership state estimation [1] F. C. Schweppe. Recursive state estimation: unknown but bounded errors and system inputs. [2] D. P. Bertsekas and I. B. Rhodes. Recursive state estimation for a set-membership description of uncertainty.

| A novel ltering methodology, recently introduced as a viable tool for adaptive signal processing , is presented for applications to interference suppression in communication systems. This technique is based on bounding the worst-case lter error by a designer-speciied value. I. Introduction This paper presents some recent work on alternative method-ologies for ltering based on a deterministic ...

We consider the non-adaptive bit-probe complexity of the set membership problem, where a set S of size at most n from a universe of size m is to be represented as a short bit vector in order to answer membership queries of the form “Is x in S?” by non-adaptively probing the bit vector at t places. Let sN (m,n, t) be the minimum number of bits of storage needed for such a scheme. Buhrman, Milter...

Measurement results contain different kinds of uncertainty. Besides systematic errors andrandom errors individual measurement results are also subject to another type of uncertainty,so-called emph{fuzziness}. It turns out that special fuzzy subsets of the set of real numbers $RR$are useful to model fuzziness of measurement results. These fuzzy subsets $x^*$ are called emph{fuzzy numbers}. The m...

The objective of this chapter is to provide a semantic framework for fuzzy sets in the theory of rough sets. Rough membership functions are viewed as a special type of fuzzy membership functions interpretable using conditional probabilities. The relationships between fuzzy membership functions and rough membership functions, between core and support of fuzzy set theory and lower and upper appro...

In this article, we continue the development of the theory of fuzzy sets [23], started with [14] with the future aim to provide the formalization of fuzzy numbers [8] in terms reflecting the current state of the Mizar Mathematical Library. Note that in order to have more usable approach in [14], we revised that article as well; some of the ideas were described in [12]. As we can actually unders...

This paper presents a fuzzy set theory based approach to Chinese sentence-level sentiment classification. Compared with traditional topic-based text classification techniques, the fuzzy set theory provides a straightforward way to model the intrinsic fuzziness between sentiment polarity classes. To approach fuzzy sentiment classification, we first propose a fine-to-coarse strategy to estimate s...