We present a comparison for several filter configurations for freeway traffic state estimation. Since the environmental conditions on a freeway may change over time (e.g., changing weather conditions), parameter estimation is also considered. We compare the performance of the extended Kalman filter and the unscented Kalman filter for state estimation, parameter estimation, joint estimation and dual estimation. Furthermore, the performance is evaluated for different detector configurations.The main conclusions from the simulations are that (1) the performance of the extended Kalman filter and the unscented Kalman filter is comparable, (2) joint filtering performs significantly better than dual filtering, and (3) a larger number of detectors results in better state estimation, but has no significant influence on the parameter estimation error.
Summary. Over the last few years, much research has been dedicated to the creation of decisions support systems for space system engineers or even for completely automated design methods capturing the reasoning of system experts. However, the problem of taking into account the uncertainties of variables and models defining an optimal and robust spacecraft design have not been tackled effectively yet. This chapter proposes a novel, simple approach based on the clouds formalism to elicit and process the uncertainty information provided by expert designers and to incorporate this information into the automated search for a robust, optimal design.
Summary. The ambitious short-term and long-term goals set down by the various national space agencies call for radical advances in several of the main space engineering areas, the design of intelligent space agents certainly being one of them. In recent years, this has led to an increasing interest in artificial intelligence by the entire aerospace community. However, in the current state of the art, several open issues and showstoppers can be identified. In this chapter, we review applications of artificial intelligence in the field of space engineering and space technology and identify open research questions and challenges. In particular, the following topics are identified and discussed: distributed artificial intelligence, enhanced situation self-awareness, and decision support for spacecraft system design.
We study learning vector quantization methods to adapt the size of (hyper-)spherical clusters to better fit a given data set, especially in the context of non-normalized activations. The basic idea of our approach is to compute a desired radius from the data points that are assigned to a cluster and then to adapt the current radius of the cluster in the direction of this desired radius. Since cluster size adaptation has a considerable impact on the number of clusters needed to cover a data set, we also examine how to select the number of clusters based on validity measures and, in the context of non-normalized activations, on the coverage of the data.
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