PROBABILISTIC PRECIPITATION MAP BASED ON AMEDAS DATA

Probabilistic self-localisation on a qualitative map based on occlusions

Spatial knowledge plays an essential role in human reasoning, permitting tasks such as locating objects in the world (including oneself), reasoning about everyday actions and describing perceptual information. This is also the case in the field of mobile robotics, where one of the most basic (and essential) tasks is the autonomous determination of the pose of a robot with respect to a map, give...

Probabilistic Topological Map and Binary data

The Self Organizing Map (SOM) proposed by Kohonen [7] is a well known neural model which provides both quantization and clustering of the observation space. In this paper, we adapt the Bernoulli mixture approach, proposed by [6], to the model of binary topological map [2] and show that using a probabilistic formalism gives rise to better quantization process and classi cation performances.

NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...

Developing a Fuzzy Logic Model to Predict Asphaltene Precipitation during Natural Depletion based on Experimental Data

Probabilistic Forecasts of Precipitation Type

Precipitation-type forecasting is the determination of when and where particular types of precipitation (e.g., snow, rain, ice pellets, freezing rain) will occur during a forecast period. Although much is already known about the physical processes that determine the type of precipitation that reaches the ground, these forecasts are very challenging for most forecasters because of inadequate atm...