12,099 research outputs found
A cloned linguistic decision tree controller for real-time path planning in hostile environments
AbstractThe idea of a Cloned Controller to approximate optimised control algorithms in a real-time environment is introduced. A Cloned Controller is demonstrated using Linguistic Decision Trees (LDTs) to clone a Model Predictive Controller (MPC) based on Mixed Integer Linear Programming (MILP) for Unmanned Aerial Vehicle (UAV) path planning through a hostile environment. Modifications to the LDT algorithm are proposed to account for attributes with circular domains, such as bearings, and discontinuous output functions. The cloned controller is shown to produce near optimal paths whilst significantly reducing the decision period. Further investigation shows that the cloned controller generalises to the multi-obstacle case although this can lead to situations far outside of the training dataset and consequently result in decisions with a high level of uncertainty. A modification to the algorithm to improve the performance in regions of high uncertainty is proposed and shown to further enhance generalisation. The resulting controller combines the high performance of MPCāMILP with the rapid response of an LDT while providing a degree of transparency/interpretability of the decision making
Violation of the sphericity assumption and its effect on Type-I error rates in repeated measures ANOVA and multi-level linear models (MLM)
This study aims to investigate the effects of violations of the sphericity
assumption on Type I error rates for different methodical approaches of
repeated measures analysis using a simulation approach. In contrast to previous
simulation studies on this topic, up to nine measurement occasions were
considered. Therefore, two populations representing the conditions of a
violation vs. a non-violation of the sphericity assumption without any
between-group effect or within-subject effect were created and 5,000 random
samples of each population were drawn. Finally, the mean Type I error rates for
Multilevel linear models (MLM) with an unstructured covariance matrix (MLM-UN),
MLM with compound-symmetry (MLM-CS) and for repeated measures analysis of
variance (rANOVA) models (without correction, with
Greenhouse-Geisser-correction, and Huynh-Feldt-correction) were computed. To
examine the effect of both the sample size and the number of measurement
occasions, sample sizes of n = 20, 40, 60, 80, and 100 were considered as well
as measurement occasions of m = 3, 6 and 9. For MLM-UN, the results illustrate
a massive progressive bias for small sample sizes (n =20) and m = 6 or more
measurement occasions. This effect could not be found in previous simulation
studies with a smaller number of measurement occasions. The mean Type I error
rates for rANOVA with Greenhouse-Geisser-correction demonstrate a small
conservative bias if sphericity was not violated, sample sizes were small (n =
20), and m = 6 or more measurement occasions were conducted. The results plead
for a use of rANOVA with Huynh-Feldt-correction, especially when the sphericity
assumption is violated, the sample size is rather small and the number of
measurement occasions is large. MLM-UN may be used when the sphericity
assumption is violated and when sample sizes are large.Comment: 14 pages, 6 figure
Learning in evolutionary environments
Not availabl
Building Cox-Type Structured Hazard Regression Models with Time-Varying Effects
In recent years, flexible hazard regression models based on penalised splines have been developed that allow us to extend the classical Cox-model via the inclusion of time-varying and nonparametric effects. Despite their immediate appeal in terms of flexibility, these models introduce additional difficulties when a subset of covariates and the corresponding modelling alternatives have to be chosen. We present an analysis of data from a specific patient population with 90-day survival as the response variable. The aim is to determine a sensible prognostic model where some variables have to be included due to subject-matter knowledge while other variables are subject to model selection. Motivated by this application, we propose a twostage stepwise model building strategy to choose both the relevant covariates and the corresponding modelling alternatives within the choice set of possible covariates simultaneously. For categorical covariates, competing modelling approaches are linear effects and time-varying effects, whereas nonparametric modelling provides a further alternative in case of continuous covariates. In our data analysis, we identified a prognostic model containing both smooth and time-varying effects
- ā¦