Home // EMERGING 2014, The Sixth International Conference on Emerging Network Intelligence // View article
Authors:
Ruppert Martin
Eric Veith
Bernd Steinbach
Keywords: artificial neural network; evolutionary algorithm; weather forecasting; smart grid
Abstract:
Evolutionary training methods for Artificial Neural Networks can escape local minima. Thus, they are useful to train recurrent neural networks for short-term weather forecasting. However, these algorithms are not guaranteed to converge fast or even converge at all due to their stochastic nature. In this paper, we present an algorithm that uses implicit gradient information and is able to train existing individuals in order to create a dynamic reproduction probability density. It allows us to train and re-train an Artificial Neural Network supervised to forecast weather conditions.
Pages: 18 to 21
Copyright: Copyright (c) IARIA, 2014
Publication date: August 24, 2014
Published in: conference
ISSN: 2326-9383
ISBN: 978-1-61208-357-5
Location: Rome, Italy
Dates: from August 24, 2014 to August 28, 2014