Patent ID: 6226549
Filing Date: 2001-05-01
Classification: G06K

Abstract:
A method for the classification of a time series, comprising the steps of:generating a signal representing a dynamic process;sampling, using a sampler, the generated signal, producing a prescribable plurality of samples;determining, using a computer, values c.sub.t.sup.n,.tau.,p,N,.epsilon. of a generalized correlation integral for at least a part of the samples;determining the value c.sub.t.sup.n,.tau.,p,N,.epsilon. of the generalized correlation integral upon employment of preceding samples and future samples;determining a functions family of an entropy function h(p, .epsilon.) from the values c.sub.t.sup.n,.tau.,p,N,.epsilon. of the generalized correlation integral, wherein the preceding samples and the future samples are respectively past and future samples in time with reference to the sample for which the value c.sub.t.sup.n,.tau.,p,N,.epsilon. of the generalized correlation integral is respectively determined;employing a plurality (p) of the steps to the future considered sample as family parameter of the functions family of the entropy function h(p,.epsilon.);employing a partition interval quantity (.epsilon.) of a data space in which the samples can be located as a running variable of the functions family of the entropy function h(p,.epsilon.); andclassifying the time series on the basis of the curve of the functions family of the entropy function h(p,.epsilon.).