Abstract
The paper presents a method for time series prediction using a complete counterpropagation network with delay kernels. Our network takes advantage of the clustering and mapping capability of the original CPN combined with dynamical elements and become able to discover and approximate the strongest topological and temporal relationships among the fields in the data. Experimental results using two chaotic time series and a set of astrophysical data validate the performance of the proposed method. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Fierascu, C. (2005). Counterpropagation with delays with applications in time series prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 533–539). https://doi.org/10.1007/11550907_84
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