Título: Spatiotemporal Patterns Estimation Using a Multilayer Perceptron Neural Network in a Solar Physics Application
Autores: Andrade, Maria Conceicão; Rios Neto, Atair; Rosa, Reinaldo R.; Sawant, Hanumant S.; Fernandes, Francisco C. R.
Resumo: In this paper we intend to evaluate the use of multilayer perceptron neural network in a spatiotemporal patterns estimation problem and to compare the performance of the Kalman Filtering with the Backpropagation and the Levenberg-Marquardt training algorithms. The study consists of applying multilayer perceptron for estimation of the solar active regions evolution using sequential soft X-ray images obtserved by the YOHKOH solar satellite telescope. The performance test in this application is done by using the mean squared error, image visualization and the Gradient Pattern Analysis (GPA) technique, using the operator for characterization of Amplitude Asymmetric Fragmentation (AAF). The AAF operator is being used for the first time in a performance test with an Artificial Neural Network (ANN) applied in spatiotemporal patterns estimation. The results confirm the efficiency and efficacy of ANN as a tool to estimate spatiotemporal patterns in this kind of application. The tests indicate that although the Kalman Filtering showed an efficacy to learn the patterns comparable to those of the Backpropagation and the Levenberg-Marquardt algorithms, it is inefficient from the computational viewpoint in the sense that it takes a longer processing time. In addition, the ANN performance validation tests confirm the utility of the AAF operator for the performance characterization of spatiotemporal patterns algorithm generators.
Código DOI: 10.21528/CBRN2003-042
Artigo em PDF: 6CBRN_042.PDF
Arquivo BibTex: 6CBRN_042.bib