Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier object using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on urban into one of four classes: Pedestrian, bicycle, passenger car, truck. Five features calculated from point cloud generated LiDAR wer...

With the rapid growth of wind power penetration into modern grids, speed forecasting plays an increasingly significant role in planning and operation electric energy systems. However, existing methods are modeled as black boxes, which very complicated cannot be written down explicitly due to complex fluctuation characteristics series. To this end, study proposes a novel direct method based on e...

Multi-class classification problems can be efficiently solved by partitioning the original problem into sub-problems involving only two classes: for each pair of classes, a (potentially small) neural network is trained using only the data of these two classes. We show how to combine the outputs of the two-class neural networks in order to obtain posterior probabilities for the class decisions. ...

In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbols) in a continuous space that helps to smooth the language model and provide good generalization...