Results 21 to 30 of about 174 (127)
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT This paper focuses on state estimation for a fairly general class of systems, involving nonlinear functions and disturbances in both the process dynamics and output equations. A nonlinear observer that satisfies a H∞$$ {\boldsymbol{H}}_{\boldsymbol{\infty}} $$ disturbance attenuation constraint in addition to providing asymptotic stability in ...
Hamidreza Movahedi +2 more
wiley +1 more source
PID‐Like Robust Control of Non‐Minimum Phase Robotic Manipulators
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT This paper proposes an output‐feedback tracking controller for non‐minimum phase nonlinear systems with unknown uncertainties and external disturbances, where not all states are measurable, and the zero dynamics are unstable. The approach combines a backstepping‐based stabilizing state‐feedback law with a cascade extended high‐gain observer ...
Mohammad Al Saaideh +2 more
wiley +1 more source
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley +1 more source
On Metric Choice in Dimension Reduction for Fréchet Regression
International Statistical Review, EarlyView.Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data.
Abdul‐Nasah Soale +3 more
wiley +1 more source
A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces
International Statistical Review, EarlyView.Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri +3 more
wiley +1 more source
Gradual Changes in Functional Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We consider the problem of detecting gradual changes in the sequence of mean functions from a not necessarily stationary functional time series. Our approach is based on the maximum deviation (calculated over a given time interval) between a benchmark function and the mean functions at different time points.
Patrick Bastian, Holger Dette
wiley +1 more source
Change Point Analysis for Functional Data Using Empirical Characteristic Functionals
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a new method to detect change points in the distribution of functional data based on integrated CUSUM processes of empirical characteristic functionals. Asymptotic results are presented under conditions allowing for low‐order moments and serial dependence in the data establishing the limiting null‐distribution of the proposed test ...
Lajos Horváth +2 more
wiley +1 more source
Nonparametric Detection of a Time‐Varying Mean
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose a nonparametric portmanteau test for detecting changes in the unconditional mean of a univariate time series which may display either long or short memory. Our approach is designed to have power against, among other things, cases where the mean component of the series displays abrupt level shifts, deterministic trending behaviour ...
Fabrizio Iacone, A. M. Robert Taylor
wiley +1 more source
Detecting Relevant Deviations From the White Noise Assumption for Non‐Stationary Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We consider the problem of detecting deviations from a white noise assumption in time series. Our approach differs from the numerous methods proposed for this purpose with respect to two aspects. First, we allow for non‐stationary time series. Second, we address the problem that a white noise test is usually not performed because one believes ...
Patrick Bastian
wiley +1 more source
Adaptive Estimation for Weakly Dependent Functional Times Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose adaptive mean and autocovariance function estimators for stationary functional time series under 𝕃p−m‐approximability assumptions. These estimators are designed to adapt to the regularity of the curves and to accommodate both sparse and dense data designs.
Hassan Maissoro +2 more
wiley +1 more source