Pii: S0167-2789(98)00168-7
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چکیده
We compare two algorithms for the numerical estimation of the correlation dimension from a finite set of vectors: the "classical" algorithm of Grassberger and Procaccia (GPA) and the recently proposed algorithm of Judd (JA). Data set size requirements and their relations to systematic and statistical errors of the estimates are investigated. It is demonstrated that correlation dimensions of the order of 6 can correctly be resolved on the basis of about 100000 data points in the case of a continuous trajectory on a strange attractor; the minimum data set size is, however, noticeably dependent on the geometrical structure of the system from which the vectors were sampled. © 1998 Elsevier Science B.V. All rights reserved. PACS: 05.45.+b
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تاریخ انتشار 1998