Explicit model predictive control accuracy analysis

Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. represented with a small number of memory bits. An aggressive quantization decreases the number of bits and the controller manufacturing costs, and may increase the speed of the controller, but reduces accuracy of the control input computation. We derive upper bounds for the absolute error in the control depending on the number of quantization bits and system parameters. The bounds can be used to determine how many quantization bits are needed in order to guarantee a specific level of accuracy in the control input.Comment: 6 pages, 7 figures. Accepted to IEEE CDC 201

Measures of Predictive Success for Rating Functions

Aim of our paper is to develop an adequate measure of predictive success and accuracy of rating functions. At first, we show that the common measures of rating accuracy, i.e. area under curve and accuracy ratio, respectively, lack of informative value of single rating classes. Selten (1991) builds up an axiomatic framework for measures of predictive success. Therefore, we introduce a measure for rating functions that fulfills the axioms proposed by Selten (1991). Furthermore, an empirical investigation analyzes predictive power and accuracy of Standard & Poor's and Moody's ratings, and compares the rankings according to area under curve and our measure.Accuracy Measure, Rating Functions, Predictive Success, Discriminative Power

Toward a Theory of Evaluating Predictive Accuracy

We suggest a theoretical basis for the comparative evaluation of forecasts. Instead of the general assumption that the data is generated from a stochastic model, we classify three stages of prediction experiments: pure non-stochastic prediction of given data, stochastic prediction of given data, and double stochastic simulation. The concept is demonstrated using an empirical example of UK investment data.Forecasting, Time series, Investment

Improved dynamic geomagnetic rigidity cutoff modeling: testing predictive accuracy

. In the polar atmosphere, significant chemical and ionization changes occur during solar proton events (SPE). The access of solar protons to this region is limited by the dynamically changing geomagnetic field. In this study we have used riometer absorption observations to investigate the accuracy of a model to predict Kp-dependent geomagnetic rigidity cutoffs, and hence the changing proton fluxes. The imaging riometer at Halley, Antarctica is ideally situated for such a study, as the rigidity cutoff sweeps back and forth across the instrument's field of view, providing a severe test of the rigidity cutoff model. Using observations from this riometer during five solar proton events, we have confirmed the basic accuracy of this rigidity model. However, we find that the model can be improved by setting a lower Kp limit (i.e., Kp=5 instead of 6) at which the rigidity modeling saturates. We also find that for L>4.5 the apparent L-shell of the beam moves equatorwards. In addition, the Sodankyla Ion and Neutral Chemistry model is used to determine an empirical relationship between integral proton precipitation fluxes and nighttime ionosphere riometer absorption, in order to allow consideration of winter time SPEs. We find that during the nighttime the proton flux energy threshold is lowered to include protons with energies of >5 MeV in comparison with >10 MeV for the daytime empirical relationships. In addition, we provide an indication of the southern and northern geographic regions inside which SPEs play a role in modifying the neutral chemistry of the stratosphere and mesosphere

Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin's frog

Indexación: Web of Science; Scopus.Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as geographical areas subjected to novel climates are expected to arise, they must be reported as they show less accurate predictions under future climate scenarios. Consequently, environmental extrapolation and dispersal processes should be explicitly incorporated to report and reduce uncertainties in temporal predictions of SDMs, respectively. Doing so, we expect to improve the reliability of the information we provide for conservation decision makers under future climate change scenarios.http://onlinelibrary.wiley.com/doi/10.1002/eap.1556/abstract;jsessionid=1E2084FF99600D0EEC9FA358A3DBC2A3.f02t0