3,644 research outputs found
Filtering and control for unreliable communication: The discrete-time case
Copyright © 2014 Guoliang Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the past decades, communication networks have been extensively employed in many practical control systems, such as manufacturing plants, aircraft, and spacecraft to transmit information and control signals between the system components. When a control loop is closed via a serial communication channel, a networked control system (NCS) is formed. NCSs have become very popular for their great advantages over traditional systems (e.g., low cost, reduced weight, and power requirements, etc.). Generally, it has been implicitly assumed that the communication between the system components is perfect; that is, the signals transmitted from the plant always arrive at the filter or controller without any information loss. Unfortunately, such an assumption is not always true. For example, a common feature of the NCSs is the presence of significant network-induced delays and data losses across the networks. Therefore, an emerging research topic that has recently drawn much attention is how to cope with the effect of network-induced phenomena due to the unreliability of the network communication. This special issue aims at bringing together the latest approaches to understand, filter, and control for discrete-time systems under unreliable communication. Potential topics include but are not limited to (a) multiobjective filtering or control, (b) network-induced phenomena, (c) stability analysis, (d) robustness and fragility, and (e) applications in real-world discrete-time systems
Asymptotic optimality and efficient computation of the leave-subject-out cross-validation
Although the leave-subject-out cross-validation (CV) has been widely used in
practice for tuning parameter selection for various nonparametric and
semiparametric models of longitudinal data, its theoretical property is unknown
and solving the associated optimization problem is computationally expensive,
especially when there are multiple tuning parameters. In this paper, by
focusing on the penalized spline method, we show that the leave-subject-out CV
is optimal in the sense that it is asymptotically equivalent to the empirical
squared error loss function minimization. An efficient Newton-type algorithm is
developed to compute the penalty parameters that optimize the CV criterion.
Simulated and real data are used to demonstrate the effectiveness of the
leave-subject-out CV in selecting both the penalty parameters and the working
correlation matrix.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1063 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Semiparametric GEE analysis in partially linear single-index models for longitudinal data
In this article, we study a partially linear single-index model for
longitudinal data under a general framework which includes both the sparse and
dense longitudinal data cases. A semiparametric estimation method based on a
combination of the local linear smoothing and generalized estimation equations
(GEE) is introduced to estimate the two parameter vectors as well as the
unknown link function. Under some mild conditions, we derive the asymptotic
properties of the proposed parametric and nonparametric estimators in different
scenarios, from which we find that the convergence rates and asymptotic
variances of the proposed estimators for sparse longitudinal data would be
substantially different from those for dense longitudinal data. We also discuss
the estimation of the covariance (or weight) matrices involved in the
semiparametric GEE method. Furthermore, we provide some numerical studies
including Monte Carlo simulation and an empirical application to illustrate our
methodology and theory.Comment: Published at http://dx.doi.org/10.1214/15-AOS1320 in the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Optimal strategies of radial velocity observations in planet search surveys
Applications of the theory of optimal design of experiments to radial
velocity planet search surveys are considered. Different optimality criteria
are discussed, basing on the Fisher, Shannon, and Kullback-Leibler
informations. Algorithms of optimal scheduling of RV observations for two
important practical problems are considered. The first problem is finding the
time for future observations to yield the maximum improvement of the precision
of exoplanetary orbital parameters and masses. The second problem is finding
the most favourable time for distinguishing alternative orbital fits (the
scheduling of discriminating observations).
These methods of optimal planning are demonstrated to be potentially
efficient for multi-planet extrasolar systems, in particular for resonant ones.
In these cases, the optimal dates of observations are often concentrated in
quite narrow time segments.Comment: 8 pages, 2 figures, no tables, Accepted to MNRA
Estimating the Spot Covariation of Asset Prices - Statistical Theory and Empirical Evidence
We propose a new estimator for the spot covariance matrix of a
multi-dimensional continuous semi-martingale log asset price process which is
subject to noise and non-synchronous observations. The estimator is constructed
based on a local average of block-wise parametric spectral covariance
estimates. The latter originate from a local method of moments (LMM) which
recently has been introduced. We prove consistency and a point-wise stable
central limit theorem for the proposed spot covariance estimator in a very
general setup with stochastic volatility, leverage effects and general noise
distributions. Moreover, we extend the LMM estimator to be robust against
autocorrelated noise and propose a method to adaptively infer the
autocorrelations from the data. Based on simulations we provide empirical
guidance on the effective implementation of the estimator and apply it to
high-frequency data of a cross-section of Nasdaq blue chip stocks. Employing
the estimator to estimate spot covariances, correlations and volatilities in
normal but also unusual periods yields novel insights into intraday covariance
and correlation dynamics. We show that intraday (co-)variations (i) follow
underlying periodicity patterns, (ii) reveal substantial intraday variability
associated with (co-)variation risk, and (iii) can increase strongly and nearly
instantaneously if new information arrives
Convergence Rate Analysis of Distributed Gossip (Linear Parameter) Estimation: Fundamental Limits and Tradeoffs
The paper considers gossip distributed estimation of a (static) distributed
random field (a.k.a., large scale unknown parameter vector) observed by
sparsely interconnected sensors, each of which only observes a small fraction
of the field. We consider linear distributed estimators whose structure
combines the information \emph{flow} among sensors (the \emph{consensus} term
resulting from the local gossiping exchange among sensors when they are able to
communicate) and the information \emph{gathering} measured by the sensors (the
\emph{sensing} or \emph{innovations} term.) This leads to mixed time scale
algorithms--one time scale associated with the consensus and the other with the
innovations. The paper establishes a distributed observability condition
(global observability plus mean connectedness) under which the distributed
estimates are consistent and asymptotically normal. We introduce the
distributed notion equivalent to the (centralized) Fisher information rate,
which is a bound on the mean square error reduction rate of any distributed
estimator; we show that under the appropriate modeling and structural network
communication conditions (gossip protocol) the distributed gossip estimator
attains this distributed Fisher information rate, asymptotically achieving the
performance of the optimal centralized estimator. Finally, we study the
behavior of the distributed gossip estimator when the measurements fade (noise
variance grows) with time; in particular, we consider the maximum rate at which
the noise variance can grow and still the distributed estimator being
consistent, by showing that, as long as the centralized estimator is
consistent, the distributed estimator remains consistent.Comment: Submitted for publication, 30 page
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
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