Sökning: "Kernel functions"
Visar resultat 1 - 5 av 58 avhandlingar innehållade orden Kernel functions.
1. A kernel function approach to exact solutions of Calogero-Moser-Sutherland type models
Sammanfattning : This Doctoral thesis gives an introduction to the concept of kernel functionsand their signicance in the theory of special functions. Of particularinterest is the use of kernel function methods for constructing exact solutionsof Schrodinger type equations, in one spatial dimension, with interactions governedby elliptic functions. LÄS MER
2. Contributions to Kernel Equating
Sammanfattning : The statistical practice of equating is needed when scores on different versions of the same standardized test are to be compared. This thesis constitutes four contributions to the observed-score equating framework kernel equating. LÄS MER
3. Weighted Bergman kernels and biharmonic Green functions
Sammanfattning : The main theme of this thesis is the connection between weighted biharmonic Green functions and weighted Bergman kernels. In the first paper, which is a joint work with H. Hedenmalm and S. Shimorin, we prove that weighted biharmonic Green functions are positive for weights which satisfy a mean-value condition and whose logarithms are subharmonic. LÄS MER
4. Bergman space methods and integral means spectra of univalent functions
Sammanfattning : We study universal integral means spectra of certain classes of univalent functions defined on subsets of the complex plane. After reformulating the definition of the integral means spectrum of a univalent function in terms of membership in weighted Bergman spaces, we describe the Hilbert space techniques that can be used to estimate universal means spectra from above. LÄS MER
5. System identification with input uncertainties : an EM kernel-based approach
Sammanfattning : Many classical problems in system identification, such as the classical predictionerror method and regularized system identification, identification of Hammersteinand cascaded systems, blind system identification, as well as errors-in-variablesproblems and estimation with missing data, can be seen as particular instancesof the general problem of the identification of systems with limited information.In this thesis, we introduce a framework for the identification of linear dynamicalsystems subject to inputs that are not perfectly known. LÄS MER