Abstract
An algorithm for the sequential analysis of multivariate data is, presented along with some experimental results. The algorithm is based upon the sequential nonlinear mapping of L-dimensional vectors from the L-hiperspace into a lower-dimensional (two-dimensional) vectors such that the inner structure of distances between the vectors is preserved. Expressions for the sequential nonlinear mapping are obtained. The sequential nonlinear mapping is applied to sequential c1usterization of random processes and creation of an essentially new method for sequential detection of many abrupt or slow changes in several unknown states of dynamic systems.
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