Analysis of time series with artificial neural networks Conference Paper

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Overview

abstract

• This paper presents the study of time series in gravitational lensing to solve the time delay problem in astro-physics. The time series are irregularly sampled and noisy. There are several methods to estimate the time delay between this kind of time series, and this paper proposes a new method based on artificial neural networks, in particular, General Regression Neural Networks (GRNN), which is based on Radial Basis Function (RBF) networks. We also compare other typical artificial neural network architectures, where the learning time of GRNN is better. We analyze artificial data used in the literature to compare the performance of the new method against state-of-the-art methods. Some statistics are presented to study the significance of results. © 2008 IEEE.

authors

• Cuevas Tello Juan Carlos
• González-Grimaldo R.A.

publication date

• 2008-01-01

published in

• 7th Mexican International Conference on Artificial Intelligence - Proceedings of the Special Session, MICAI 2008  Proceedings

Research

keywords

• General regression neural networks; Neural networks; Radial basis functions; Time series Artificial intelligence; Backpropagation; Feedforward neural networks; Functions; Image segmentation; Medical imaging; Metropolitan area networks; Probability density function; Radial basis function networks; Regression analysis; Time delay; Time series analysis; Vegetation; Art methods; Artificial datums; Artificial neural networks; General regression neural networks; Gravitational lensing; Learning times; Radial basis functions; Time series; Time- delays; Neural networks

Identity

Digital Object Identifier (DOI)

• 10.1109/MICAI.2008.55

Additional Document Info

start page

• 131

end page

• 137