Stochastic Optimization in Robust Statistic

2000 Mathematics Subject Classification: 62J05, 62G35

Comparação de carteiras otimizadas segundo o critério média-variância formadas através de estimativas robustas de risco e retorno

Este trabalho examinou as características de carteiras compostas por ações e otimizadas segundo o critério de média-variância e formadas através de estimativas robustas de risco e retorno. A motivação para isto é a distribuição típica de ativos financeiros (que apresenta outliers e mais curtose que a distribuição normal). Para comparação entre as carteiras, foram consideradas suas propriedades: estabilidade, variabilidade e os índices de Sharpe obtidos pelas mesmas. O resultado geral mostra que estas carteiras obtidas através de estimativas robustas de risco e retorno apresentam melhoras em sua estabilidade e variabilidade, no entanto, esta melhora é insuficiente para diferenciar os índices de Sharpe alcançados pelas mesmas das carteiras obtidas através de método de máxima verossimilhança para estimativas de risco e retorno.

A robust nonlinear identification algorithm using PRESS statistic and forward regression

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.

Robust identification for linear-in-the-parameters models

In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness, including three algorithms using combined A- or D-optimality or PRESS statistic (Predicted REsidual Sum of Squares) with regularised orthogonal least squares algorithm respectively. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalisation scheme in orthogonal least squares or regularised orthogonal least squares has been extended such that the new algorithms are computationally efficient. A numerical example is included to demonstrate effectiveness of the algorithms. Copyright (C) 2003 IFAC.

Robust nonlinear model identification methods using forward regression

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.

Chemical Immobilization Of Crosslinked Polymeric Ionic Liquids On Nitinol Wires Produces Highly Robust Sorbent Coatings For Solid-phase Microextraction.

Super elastic nitinol (NiTi) wires were exploited as highly robust supports for three distinct crosslinked polymeric ionic liquid (PIL)-based coatings in solid-phase microextraction (SPME). The oxidation of NiTi wires in a boiling (30%w/w) H2O2 solution and subsequent derivatization in vinyltrimethoxysilane (VTMS) allowed for vinyl moieties to be appended to the surface of the support. UV-initiated on-fiber copolymerization of the vinyl-substituted NiTi support with monocationic ionic liquid (IL) monomers and dicationic IL crosslinkers produced a crosslinked PIL-based network that was covalently attached to the NiTi wire. This alteration alleviated receding of the coating from the support, which was observed for an analogous crosslinked PIL applied on unmodified NiTi wires. A series of demanding extraction conditions, including extreme pH, pre-exposure to pure organic solvents, and high temperatures, were applied to investigate the versatility and robustness of the fibers. Acceptable precision of the model analytes was obtained for all fibers under these conditions. Method validation by examining the relative recovery of a homologous group of phthalate esters (PAEs) was performed in drip-brewed coffee (maintained at 60 °C) by direct immersion SPME. Acceptable recoveries were obtained for most PAEs in the part-per-billion level, even in this exceedingly harsh and complex matrix.

Fluorescence images combined to statistic test for fingerprinting of citrus plants after bacterial infection

The citrus greening (or huanglongbing) disease has caused serious problems in citrus crops around the world. An early diagnostic method to detect this malady is needed due to the rapid dissemination of Candidatus Liberibacter asiaticus (CLas) in the field. This analytical study investigated the fluorescence responses of leaves from healthy citrus plants and those inoculated with CLas by images from a stereomicroscope and also evaluated their potential for the early diagnosis of the infection caused by this bacterium. The plants were measured monthly, and the evolution of the bacteria on inoculated plants was monitored by real-time quantitative polymerase chain reaction (RT-qPCR) amplification of CLas sequences. A statistical method was used to analyse the data. The selection of variables from histograms of colours (colourgrams) of the images was optimized using a paired Student's t-test. The intensity of counts for green colours from images of fluorescence had clearly minor variations for healthy plants than diseased ones. The darker green colours were the indicators of healthy plants and the light colours for the diseased. The method of fluorescence images is novel for fingerprinting healthy and diseased plants and provides an alternative to the current method represented by PCR and visual inspection. A new, non-subjective pattern of analysis and a non-destructive method has been introduced that can minimize the time and costs of analyses.

Robust relations between CCN and the vertical evolution of cloud drop size distribution in deep convective clouds

In-situ measurements in convective clouds (up to the freezing level) over the Amazon basin show that smoke from deforestation fires prevents clouds from precipitating until they acquire a vertical development of at least 4 km, compared to only 1-2 km in clean clouds. The average cloud depth required for the onset of warm rain increased by similar to 350 m for each additional 100 cloud condensation nuclei per cm(3) at a super-saturation of 0.5% (CCN0.5%). In polluted clouds, the diameter of modal liquid water content grows much slower with cloud depth (at least by a factor of similar to 2), due to the large number of droplets that compete for available water and to the suppressed coalescence processes. Contrary to what other studies have suggested, we did not observe this effect to reach saturation at 3000 or more accumulation mode particles per cm(3). The CCN0.5% concentration was found to be a very good predictor for the cloud depth required for the onset of warm precipitation and other microphysical factors, leaving only a secondary role for the updraft velocities in determining the cloud drop size distributions. The effective radius of the cloud droplets (r(e)) was found to be a quite robust parameter for a given environment and cloud depth, showing only a small effect of partial droplet evaporation from the cloud's mixing with its drier environment. This supports one of the basic assumptions of satellite analysis of cloud microphysical processes: the ability to look at different cloud top heights in the same region and regard their r(e) as if they had been measured inside one well developed cloud. The dependence of r(e) on the adiabatic fraction decreased higher in the clouds, especially for cleaner conditions, and disappeared at r(e)>=similar to 10 mu m. We propose that droplet coalescence, which is at its peak when warm rain is formed in the cloud at r(e)=similar to 10 mu m, continues to be significant during the cloud's mixing with the entrained air, cancelling out the decrease in r(e) due to evaporation.

Robust STATCOM control for the stabilisation of fixed-speed wind turbines during low voltages

This paper presents a robust voltage control scheme for fixed-speed wind generators using a static synchronous compensator (STATCOM) controller. To enable a linear and robust control framework with structured uncertainty, the overall system is represented by a linear part plus a nonlinear part that covers an operating range of interest required to ensure stability during severe low voltages. The proposed methodology is flexible and readily applicable to larger wind farms of different configurations. The performance of the control strategy is demonstrated on a two area test system. Large disturbance simulations demonstrate that the proposed controller enhances voltage stability as well as transient stability of induction generators during low voltage ride through (LVRT) transients and thus enhances the LVRT capability. (C) 2011 Elsevier Ltd. All rights reserved.

Robust state prediction for descriptor systems

This paper deals with the problem of state prediction for descriptor systems subject to bounded uncertainties. The problem is stated in terms of the optimization of an appropriate quadratic functional. This functional is well suited to derive not only the robust predictor for descriptor systems but also that for usual state-space systems. Numerical examples are included in order to demonstrate the performance of this new filter. (C) 2008 Elsevier Ltd. All rights reserved.

Experimental investigation on adaptive robust controller designs applied to a free-floating space manipulator

This paper aims to formulate and investigate the application of various nonlinear H(infinity) control methods to a fiee-floating space manipulator subject to parametric uncertainties and external disturbances. From a tutorial perspective, a model-based approach and adaptive procedures based on linear parametrization, neural networks and fuzzy systems are covered by this work. A comparative study is conducted based on experimental implementations performed with an actual underactuated fixed-base planar manipulator which is, following the DEM concept, dynamically equivalent to a free-floating space manipulator. (C) 2011 Elsevier Ltd. All rights reserved.

Robust Control of Flight Simulator Motion Base

The purpose of this study is to apply robust inverse dynamics control for a six-degree-of-freedom flight simulator motion system. From an implementation viewpoint, simplification of the inverse dynamics control law is introduced by assuming control law matrices as constants. The robust control strategy is applied in the outer loop of the inverse dynamic control to counteract the effects of imperfect compensation due this simplification. The control strategy is designed using the Lyapunov stability theory. Forward and inverse kinematics and a full dynamic model of a six-degree-of-freedom motion base driven by electromechanical actuators are briefly presented. A describing function, acceleration step response and some maneuvers computed from the washout filter were used to evaluate the performance of the controllers.

Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Robust model predictive controller with output feedback and target tracking

A model predictive controller (MPC) is proposed, which is robustly stable for some classes of model uncertainty and to unknown disturbances. It is considered as the case of open-loop stable systems, where only the inputs and controlled outputs are measured. It is assumed that the controller will work in a scenario where target tracking is also required. Here, it is extended to the nominal infinite horizon MPC with output feedback. The method considers an extended cost function that can be made globally convergent for any finite input horizon considered for the uncertain system. The method is based on the explicit inclusion of cost contracting constraints in the control problem. The controller considers the output feedback case through a non-minimal state-space model that is built using past output measurements and past input increments. The application of the robust output feedback MPC is illustrated through the simulation of a low-order multivariable system.

Robust model predictive control with zone control

Model predictive control (MPC) is usually implemented as a control strategy where the system outputs are controlled within specified zones, instead of fixed set points. One strategy to implement the zone control is by means of the selection of different weights for the output error in the control cost function. A disadvantage of this approach is that closed-loop stability cannot be guaranteed, as a different linear controller may be activated at each time step. A way to implement a stable zone control is by means of the use of an infinite horizon cost in which the set point is an additional variable of the control problem. In this case, the set point is restricted to remain inside the output zone and an appropriate output slack variable is included in the optimisation problem to assure the recursive feasibility of the control optimisation problem. Following this approach, a robust MPC is developed for the case of multi-model uncertainty of open-loop stable systems. The controller is devoted to maintain the outputs within their corresponding feasible zone, while reaching the desired optimal input target. Simulation of a process of the oil re. ning industry illustrates the performance of the proposed strategy.