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Reconstruction of chaotic time series by neural models: a case study

✍ Scribed by Stefania Tronci; Massimiliano Giona; Roberto Baratti


Book ID
114296577
Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
276 KB
Volume
55
Category
Article
ISSN
0925-2312

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A new method is presented to study the local divergence of trajectories in very short time series, as one of the traits that can help to distinguish deterministic chaos from autocorrelated noise. It avoids some of the problems associated with the estimation of Lyapunov exponents. It consists of test