Learnable interpolation and extrapolation network for fuzzy pulmonary lobe segmentation

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines Domonkos Tikk∗1, Zsolt Csaba Johanyák∗2, Szilveszter Kovács∗3, and Kok Wai Wong∗4 ∗1Dep. of Telecommunications and Media Informatics, Budapest University of Technology and Economics Magyar tudósok krt. 2, H-1117 Budapest, Hungary E-mail: [email protected] ∗2Institute of Information Technology, Kecskemét Col...

Extrapolation Method for Fuzzy Dierential Inclusions

Learnable Swendsen-Wang Cuts for Image Segmentation

We propose a framework for Bayesian unsupervised image segmentation with descriptive, learnable models. Our approach is based on learning descriptive models for segmentation and applying Monte Carlo Markov chain to traverse the solution space. Swendsen-Wang cuts are adapted to make meaningful jumps in solution space.

Representing membership functions as points in high-dimensional spaces for fuzzy interpolation and extrapolation

This paper introduces a new approach for fuzzy interpolation and extrapolation of sparse rule base comprising of membership functions with finite number of characteristic points. The approach calls for representing membership functions as points in high-dimensional Cartesian spaces using the locations of their characteristic points as coordinates. Hence, a fuzzy rule base can be viewed as a set...

Stability of Barycentric Interpolation Formulas for Extrapolation

The barycentric interpolation formula defines a stable algorithm for evaluation at points in [−1, 1] of polynomial interpolants through data on Chebyshev grids. Here it is shown that for evaluation at points in the complex plane outside [−1, 1], the algorithm becomes unstable and should be replaced by the alternative modified Lagrange or “first barycentric” formula dating to Jacobi in 1825. Thi...