Optimizing Markovian modeling of chaotic systems with recurrent neural networks

Adelmo L. Cechin, Denise R. Pechmann and Luiz P.L. de Oliveira

Chaos, Solitons & Fractals, 2008, vol. 37, issue 5, 1317-1327

Abstract: In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included.

Date: 2008
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:37:y:2008:i:5:p:1317-1327

DOI: 10.1016/j.chaos.2006.10.018

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