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Optimal and Robust Estimation

today's high-performance systems. A Classic Revisited Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second;

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Robust Kalman Filtering for Signals and Systems With Large Uncertainties

cover the theory of robust state estimation for the case in which the process model contains significant uncertainties and non-linearities.;

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Estimation of Stochastic Processes with Missing Observations

We propose results of the investigation of the problem of mean square optimal estimation of linear functionals constructed from unobserved;

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An Engineering Approach to Optimal Control and Estimation Theory

Optimal control and optimal estimation are methods used to estimate unknown variables such as velocity and altitude, and are essential to;

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Optimal State Estimation

filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and;

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Robust Control Design: An Optimal Control Approach

Comprehensive and accessible guide to the three main approaches to robust control design and its applications Optimal control is a;

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Distributionally Robust Learning

structure in the predictors; (iii) distributionally robust multi-output regression and multiclass classification; (iv) optimal decision making that;

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Estimates of Periodically Correlated Isotropic Random Fields

densities and the minimax-robust spectral characteristics of the optimal estimates of functionals are proposed in the case of spectral uncertainty;

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High-Resolution and Robust Signal Processing

of parametric high-resolution estimation and detection techniques; robust array processing solutions for adaptive beam forming and high-resolution;

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Periodic Systems

robust stability; the problem of state estimation in its various forms, filtering, prediction and smoothing; control design methods, particularly;

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Robustness Theory and Application

A preeminent expert in the field explores new and exciting methodologies in the ever-growing field of robust statistics Used to develop;

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Introduction to Robust Estimation and Hypothesis Testing

in different situations. Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications;

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Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems

, advanced robust observer structures are presented. Parameter estimation based techniques are discussed as well. A particular attention is drawn to;

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Linear and Non-Linear System Theory

Linear and Non-Linear System Theory focuses on the basics of linear and non-linear systems, optimal control and optimal estimation with an;

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GPS-Based Optimal FIR Filtering of Clock Models

This book addresses novel results in the field of optimal finite impulse response (FIR) estimation and steering of the local clock time;

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Optimal Estimation Of Parameters

capacity estimator. This approach allows the optimal estimation of real-valued parameters, their number and intervals, as well as providing common;

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Optimal Estimation of Dynamic Systems

Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics;

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Stable Adaptive Control And Estimation For Nonlinear Systems

* Includes a solution manual for problems.* Provides MATLAB code for examples and solutions.* Deals with robust systems in both theory and;

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Algorithms for Regression and Classification

regression, new exact algorithms, results for robust online scale estimation, and an evolutionary computation algorithm for different estimators;

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Linear Systems

Balancing rigorous theory with practical applications, Linear Systems: Optimal and Robust Control explains the concepts behind linear;

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Multivariate Density Estimation

mixture estimation, robust parametric modeling algorithms, and clustering * More than 130 problems to help readers reinforce the main concepts and;

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Simulation and Robust Statistics

This book addresses the topics statistical simulation and robust estimation. It is accompanied by several software packages for the open;

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Testing For Normality

Describes the selection, design, theory, and application of tests for normality. Covers robust estimation, test power, and univariate and;

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Testing For Normality

Describes the selection, design, theory, and application of tests for normality. Covers robust estimation, test power, and univariate and;

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Optimal Design

in practice and it often happens that designs which are optimal for parameter estimation allow no possibility of model validation. For this reason the;

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Optimal Mixture Experiments

linear and quadratic regression models. This is followed by a review of the available literature on estimation of parameters in mixture models;

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Continuous Time Dynamical Systems

, Continuous Time Dynamical Systems: State Estimation and Optimal Control with Orthogonal Functions, considers different classes of systems with;

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