In a method for detecting the modes of a dynamic system with a large number of modes that each have a set .alpha. (t) of characteristic system parameters, a time series of at least one system variable x(t) is subjected to modeling, for example switch segmentation, so that in each time segment of a predetermined minimum length a predetermined prediction model, for example a neural network, for a system mode is detected for each system variable x(t), whereby modeling of the time series is followed by drift segmentation in which, in each time segment in which there is transition of the system from a first system mode to a second system mode, a series of mixed prediction models is detected produced by linear, paired superimposition of the prediction models of the two system modes.

 
Web www.patentalert.com

> Dynamical method for obtaining global optimal solution of general nonlinear programming problems

~ 00379