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Cubic splines with minimal norm

Jiří Kobza (2002)

Applications of Mathematics

Natural cubic interpolatory splines are known to have a minimal L 2 -norm of its second derivative on the C 2 (or W 2 2 ) class of interpolants. We consider cubic splines which minimize some other norms (or functionals) on the class of interpolatory cubic splines only. The cases of classical cubic splines with defect one (interpolation of function values) and of Hermite C 1 splines (interpolation of function values and first derivatives) with spline knots different from the points of interpolation are discussed....

Data compression with Σ Π -approximations based on splines

Olga E. Baklanova, Vladimir A Vasilenko (1993)

Applications of Mathematics

The paper contains short description of Σ Π -algorithm for the approximation of the function with two independent variables by the sum of products of one-dimensional functions. Some realizations of this algorithm based on the continuous and discrete splines are presented here. Few examples concerning with compression in the solving of approximation problems and colour image processing are described and discussed.

Design of an adaptive controller of LQG type: spline-based approach

Tatiana V. Guy, Miroslav Kárný (2000)

Kybernetika

The paper presents an alternative approach to the design of a hybrid adaptive controller of Linear Quadratic Gaussian (LQG) type for linear stochastic controlled system. The approach is based on the combination standard building blocks of discrete LQG adaptive controller with the non-standard technique of modelling of a controlled system and spline approximation of involved signals. The method could be of interest for control of systems with complex models, in particular distributed parameter systems....

Discrete smoothing splines and digital filtration. Theory and applications

Jiří Hřebíček, František Šik, Vítězslav Veselý (1990)

Aplikace matematiky

Two universally applicable smoothing operations adjustable to meet the specific properties of the given smoothing problem are widely used: 1. Smoothing splines and 2. Smoothing digital convolution filters. The first operation is related to the data vector r = ( r 0 , . . . , r n - 1 ) T with respect to the operations 𝒜 , and to the smoothing parameter α . The resulting function is denoted by σ α ( t ) . The measured sample r is defined on an equally spaced mesh Δ = { t i = i h } i = 0 n - 1 ...

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