Search results for "Spline"

Maximale Konvergenzordnung bei der numerischen Lösung von Anfangswertproblemen mit Splines

1982

In [10] a general procedureV is presented to obtain spline approximations by collocation for the solutions of initial value problems for first order ordinary differential equations. In this paper the attainable order of convergence with respect to the maximum norm is characterized in dependence of the parameters involved inV; in particular the appropriate choice of the collocation points is considered.

On the stability of spline-collocation methods of multivalue type

1987

In this paper the general classV of spline-collocation methods for first order systems of ordinary differential equations is investigated. The methods can in part be regarded as so-called multivalue methods. This type contains the generalized singly-implicit methods treated by Butcher.

A-stable spline-collocation methods of multivalue type

1989

In this paper the general classV of spline-collocation methods presented by Multhei is investigated. The methods ofV approximate solutions of first order initial value problems. ClassV contains as subclass the methods of so-called multivalue type, and in particular contains the generalized singly-implicit methods treated by Butcher.

Experimental study on B-spline-based modulation schemes applied in multilevel inverters for electric drive applications

2019

This work presents the design, simulation, and experimental validation of new B-Spline-based modulation techniques applied to a Multilevel Power Inverter (MPI), particularly focusing the attention on the harmonic content of the output voltages of the inverter. Simulation and experimental results are proposed and discussed, mainly describing the potential benefits, such as the increase of the multi-level operation of the converter, and drawbacks (low-order harmonics) related to the adoption of B-Spline functions for multilevel inverters applied in the field of electrical drives.

Cell-Average Multiwavelets Based on Hermite Interpolation

2007

Computational Aspects in Spaces of Bivariate Polynomial of w-Degree n

2005

Multivariate ideal interpolation schemes are deeply connected with H-bases. Both the definition of a H-basis and of an ideal interpolation space depend of the notion of degree used in the grading decomposition of the polynomial spaces. We studied, in the case of bivariate polynomials, a generalized degree, introduced by T. Sauer and named w-degree. This article give some theoretical results that allow us to construct algorithms for calculus of the dimension of the homogeneous spaces of bivariate polynomials of w – degree n. We implemented these algorithms in C++ language. The analysis of the results obtained, leads us to another theoretical conjecture which we proved in the end.

The λ-Error Order in Multivariate Interpolation

2005

The aim of this article is to introduce and to study a generalization of the error order of interpolation, named λ – error order of interpolation. This generalization makes possible a deeper analysis of the error in the interpolation process. We derived the general form of the λ – error order of interpolation and then we applied it for many choices of the functional λ.

Error analysis for a special X-spline

1979

Clenshaw and Negus [1] defined the cubic X-spline, and they applied it to an interpolation problem. In the present paper, for the same interpolation problem, an interpolating splinew is considered by combining two specialX-splines. The construction ofw is such that the computational labour for its determination, in the case of piecewise equally spaced knots, is less than that of the conventional cubic splines c . A complete error analysis ofw is done. One of the main results is that, in the case of piecewise equally spaced knots,w ands c have essentially the same error estimates.

Polynomial Smoothing Splines

2014

Interpolating splines is a perfect tool for approximation of a continuous-time signal \(f(t)\) in the case when samples \(x[k]=f(k),\;k\in \mathbb {Z}\) are available. However, frequently, the samples are corrupted by random noise. In such case, the so-called smoothing splines provide better approximation. In this chapter we describe periodic smoothing splines in one and two dimensions. The SHA technique provides explicit expression of such splines and enables us to derive optimal values of the regularization parameters.

Introduction: Periodic Signals and Filters

2018

In this chapter we briefly outline some well-known facts about Discrete-time periodic signals, their transforms and periodic digital filters and filter banks. For details we refer to the classical textbook A. V. Oppenheim and R. W. Schafer (Discrete-Time Signal Processing, Prentice Hall, New York, 2010, [3]) and Volume I of our book (Averbuch, Neittaanmaki and Zheludev, Spline and Spline Wavelet Methods with Applications to Signal and Image Processing, Periodic Splines, vol. 1 (Springer, Berlin, 2014)) [1] Throughout the volume, unless other indicated, \(N=2^{j}, \;j\in \mathbb {N}\).