Neural Networks for Postprocessing Model Output: ARPS

Neural Networks for Post-processing Model Output: ARPS

The temperature forecasts of The Advanced Regional Prediction System are post-processed by a neural network. Specifically, 31 stations are considered, and for each a neural network is developed. The nine input variables to the neural network are: Forecast hour, model forecast temperature, relative humidity, wind direction and speed, mean sea level pressure, cloud cover, and precipitation rate a...

Developing an Artificial Neural Networks Model for Predicting Output Energy and GHG Emission of Strawberry Production

Output Range Analysis for Deep Neural Networks

Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs are also been deployed in high-assurance applications. Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied. In this paper, ...

Recurrent neural networks for nonlinear output regulation

Based on a power-series approximation method, recurrent neural networks (RNN) are proposed for real-time synthesis and auto-tuning of feedback controllers for nonlinear output regulation systems. The proposed neurocontrol approach represents a novel application of recurrent neural networks to the nonlinear output regulation problem. The proposed approach completely inherits the stability and as...

Page Makeup by Postprocessing Text Formatter Output

Automatic text formatters usually produce poor page layouts: pages may have different lengths, widow lines abound, and figure placement is hard to control and often wrong. This paper describes a different approach to page makeup. \We add to the output of a text formatter extra information that tells how various elements of the document should appear on the page. A postprocessor uses this inform...