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Similarity in fuzzy reasoning.

Frank Klawonn, Juan Luis Castro (1995)

Mathware and Soft Computing

Fuzzy set theory is based on a `fuzzification' of the predicate in (element of), the concept of membership degrees is considered as fundamental. In this paper we elucidate the connection between indistinguishability modelled by fuzzy equivalence relations and fuzzy sets. We show that the indistinguishability inherent to fuzzy sets can be computed and that this indistinguishability cannot be overcome in approximate reasoning. For our investigations we generalize from the unit interval as the basis...

Simultaneous Localization And Mapping: A feature-based probabilistic approach

Piotr Skrzypczyński (2009)

International Journal of Applied Mathematics and Computer Science

This article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions...

Smooth and sharp thresholds for random k -XOR-CNF satisfiability

Nadia Creignou, Hervé Daudé (2003)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

The aim of this paper is to study the threshold behavior for the satisfiability property of a random k -XOR-CNF formula or equivalently for the consistency of a random Boolean linear system with k variables per equation. For k 3 we show the existence of a sharp threshold for the satisfiability of a random k -XOR-CNF formula, whereas there are smooth thresholds for k = 1 and k = 2 .

Software cost estimation with fuzzy inputs: Fuzzy modelling and aggregation of cost drivers

Miguel-Ángel Sicilia, Juan-J. Cuadrado-Gallego, Javier Crespo, Elena García Barriocanal (2005)

Kybernetika

Parametric software cost estimation models are well-known and widely used estimation tools, and several fuzzy extensions have been proposed to introduce a explicit handling of imprecision and uncertainty as part of them. Nonetheless, such extensions do not consider two basic facts that affect the inputs of software cost parametric models: cost drivers are often expressed through vague linguistic categories, and in many cases cost drivers are better expressed in terms of aggregations of second-level...

Solving maximum independent set by asynchronous distributed hopfield-type neural networks

Giuliano Grossi, Massimo Marchi, Roberto Posenato (2006)

RAIRO - Theoretical Informatics and Applications

We propose a heuristic for solving the maximum independent set problem for a set of processors in a network with arbitrary topology. We assume an asynchronous model of computation and we use modified Hopfield neural networks to find high quality solutions. We analyze the algorithm in terms of the number of rounds necessary to find admissible solutions both in the worst case (theoretical analysis) and in the average case (experimental Analysis). We show that our heuristic is better than the...

Some key research problems in automated theorem proving for hardware and software verification.

Matt Kaufmann, J. Strother Moore (2004)

RACSAM

This paper sketches the state of the art in the application of mechanical theorem provers to the verification of commercial computer hardware and software. While the paper focuses on the theorem proving system ACL2, developed by the two authors, it references much related work in formal methods. The paper is intended to satisfy the curiosity of readers interested in logic and artificial intelligence as to the role of mechanized theorem proving in hardware and software design today. In addition,...

Some methods of constructing kernels in statistical learning

Tomasz Górecki, Maciej Łuczak (2010)

Discussiones Mathematicae Probability and Statistics

This paper is a collection of numerous methods and results concerning a design of kernel functions. It gives a short overview of methods of building kernels in metric spaces, especially R n and S n . However we also present a new theory. Introducing kernels was motivated by searching for non-linear patterns by using linear functions in a feature space created using a non-linear feature map.

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