الصفحة 1
الصفحة 1
VI Hotine-Marussi Symposium on Theoretical and Computational Geodesy ; IAG Symposium Wuhan, China 29 May - 2 June, 2006
Cover almost every topic of geodesy, with particular emphasis on satellite gravity modelling, geodynamics, GPS data processing and applications, statistical estimation and prediction theory, and geodetic inverse problem theory and geodetic boundary value problems.
Regularized system identification: learning dynamic models from data
Provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods.
Point Estimation of Root Finding Methods
Sets out to state computationally verifiable initial conditions for predicting the immediate appearance of the guaranteed and fast convergence of iterative root finding methods. Attention is paid to iterative methods for simultaneous determination of polynomial zeros in the spirit of Smale's point estimation theory, introduced in 1986. Some basic concepts and Smale's theory for Newton's method, together with its modifications and higher-order methods, are presented in the first two chapters. The remaining chapters contain the recent author's results on initial conditions guaranteing convergence of a wide class of iterative methods for solving algebraic equations.
Constrained Control and Estimation : An Optimisation Approach
Using the principal tools of prediction and optimisation, this work gives the examples of how to deal with constraints, placing emphasis on model predictive control. It contains results that combine a number of methods, enabling you to build on your background in estimation theory, linear control, stability theory and state-space methods.
Linear Estimation and Detection in Krylov Subspaces
Focuses on the foundations of linear estimation theory which is essential for effective signal processing. In its first part, it gives a comprehensive overview of several key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Based on the derivation of the multistage Wiener filter in its most general form, the relationship between statistical signal processing and numerical mathematics is presented. In the second part, the theory is applied to iterative multiuser detection receivers (Turbo equalization) which are typically desired in wireless communication systems.
