A Reproducing Kernel Hilbert Space Approach To Spline Problems With Applications In Estimation And Control

A Reproducing Kernel Hilbert Space Approach to Spline Problems with Applications in Estimation and Control

The solutions to several optimization problems involve generalized spline functions. Existing algorithms for calculating splines cannot be easily updated with the addition of new data, and are therefore not suitable for real-time computation when data are acquired sequentially. In the thesis, recursive algorithms are developed for the problems of optimal interpolation and smoothing, and optimal approximation of linear functionals, when the underlying space of functions is a reproducing kernel Hilbert space. It is shown that these deterministic problems have equivalent stochastic least-squares estimation problems, and that the recursive solution of each deterministic problem corresponds to the recursive solution of the associated stochastic problem in which a discrete innovation sequence is computed. In addition, it is shown that the problem of computing the minimum-energy control of a linear time-varying system that yields an output satisfying certain functional constraints is a spline problem and can be solved recursively using the above methods. (Author).

Optimization Techniques IFIP Technical Conference

Applied Time Series Analysis II

Applied Time Series Analysis II contains the proceedings of the Second Applied Time Series Symposium Held in Tulsa, Oklahoma, on March 3-5, 1980. The symposium provided a forum for discussing significant advances in time series analysis and signal processing. Effective alternatives to the familiar least-square and maximum likelihood procedures are described, along with maximum likelihood procedures for modeling irregularly sampled series and for classifying non-stationary series. Comprised of 22 chapters, this volume begins with an introduction to the multidimensional filtering theory and presents specific case histories related to the multidimensional recursive filter stability problem; the least squares inverse problem; realization of filters; and spectral estimation. The unique properties of the three-dimensional wave equation are also considered. Subsequent chapters focus on high-resolution spectral estimators; time series analysis of geophysical inverse scattering problems; minimum entropy deconvolution; and fitting of a continuous time autoregression to discrete data. This monograph will appeal to students and practitioners in the fields of mathematics and statistics, electrical and electronics engineering, and information and computer sciences.