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About 10,000 papers for “Discretization”

Discretization

Fabian M. Uriarte · Institution of Engineering and Technology eBooks · 2013

This chapter examines the mathematical concept of discretization and its application to power system simulation. Discretization is a transformation by which continuous differential equations are transformed into difference algebraic equations. The importance of discretization is that it allows to produce solutions at and between time grid divisions.

Cited by 0

Discretization

Salvador García, Julián Luengo, Francisco Herrera · Intelligent systems reference library · 2014

Cited by 6

Discretization

Dietmar Schroeder · Computational microelectronics · 1994

Cited by 1

discretization

Patrizio Colaneri, Stephen Kirkl, Robert Shorten · 2013

Cited by 0

Discretization

Janne M. J. Huttunen, Hanna K. Pikkarainen · 2014

Cited by 0

Deriving fuzzy discretization from interval discretization

Hisao Ishibuchi, Takeshi Yamamoto · 2004

We examine two methods for deriving fuzzy discretization from interval discretization for classification problems. One method uses linear membership functions with trapezoidal shape. The other extends them to piecewise linear functions. In both methods, we use a control parameter that specifies the overlap grade between adjacent membership functions. The overlap grade can be viewed as the fuzzification grade of interval discretization. Through computer simulations, we examine three interval discretization methods from which fuzzy discretization is derived: equal width intervals, equal frequency intervals, and minimum entropy intervals. We extract fuzzy rules for wine data and sonar data with many continuous attributes. Simulation results show that the classification ability of fuzzy rules is improved in some cases and degraded in other cases by the increase in the fuzzification grade.

Cited by 10

Weight discretization

Dasbach, Stefan, Tetzlaff, Tom, Diesmann, Markus et al. · Zenodo (CERN European Organization for Nuclear Research) · 2021

This repository contains the code used to create all figures of the manuscript:<br> Stefan Dasbach, Tom Tetzlaff, Markus Diesmann and Johanna Senk<br> "Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution"

Cited by 0Open access

Time discretization

Gustaf Söderlind · Springer series in computational mathematics · 2024

Cited by 0

Full Discretization

Willy Dörfler, Marlis Hochbruck, Jonas Köhler et al. · Oberwolfach Seminars · 2022

Cited by 0

Weight discretization

Dasbach, Stefan, Tetzlaff, Tom, Diesmann, Markus et al. · Zenodo (CERN European Organization for Nuclear Research) · 2021

This repository contains the code used to create all figures of the manuscript:<br> Stefan Dasbach, Tom Tetzlaff, Markus Diesmann and Johanna Senk<br> "Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution"

Cited by 0Open access

Discretization Methods

2012

This chapter describes various important discretization methods. The chapter discusses convergence issues and presents four processes that are used as illustrations. Ito-Taylor schemes in Q-dimensions and the forms taken by them in 1-dimension are presented. The predictor–corrector schemes are introduced and the approximations are applied to two of the illustrative processes, which benchmarks with the geometric Brownian motion (GBM) and Vasicek processes. All the Ito-Taylor based schemes in the benchmark cases work well and higher order convergence is achieved. The Euler discretization usually works for processes met in finance but converges only slowly with possibly high error. However, higher order Ito–Taylor schemes are complex and they can work well when they work at all.

Cited by 0

Morphological discretization

Heijmans, Henk · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · 1991

Cited by 7Open access