Informatica

An essentially new method for discrete sequential detection of abrupt or slow multiple changes in several unknown properties of random processes is considered. The method is based on a sequential nonlinear mapping into two-dimensional vectors of many-dimensional vectors of parameters which describe the properties of random process. The mapping error function is chosen and the expressions for sequential nonlinear mapping are presented along with some experimental results. Theoretical minimum amount of at the very beginning simultaneously mapped vectors is obtained.

An essentially new method for sequential detection of many abrupt or slow changes in several unknown states of dynamic systems is presented. This method is based on the sequential nonlinear mapping into two-dimensional vectors of many-dimensional vectors which describe the present system states. The expressions for sequential nonlinear mapping are obtained. The mapping preserves the inner structure of distances between the vectors. Examples are given.

The problem multialternative recognition of non-stationary processes on the basis of dynamic models is investigated in the paper. The algorithms of pointwise and group classifications are compared. Clustering algorithms based on nonlinear mapping of the segments of random processes onto the plain are used to construct the classifiers.