Method for detecting time dependent modes of dynamic systems

Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression

Reexamination Certificate

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C706S006000, C706S016000, C706S022000, C706S044000, C703S006000, C703S011000, C703S013000

Reexamination Certificate

active

09508042

ABSTRACT:
In a method for detecting the modes of a dynamic system with a large number of modes that each have a set α (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.

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Andreas Draeger et al.,Prādiktive Regelung Verfahrenstechnischer Anlagen Mit Neuronalen Netzen, Automatisierungstechnische Praxis 37, 1995, 4, pp. 55-61 w/English language summary.
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Stephan Ester,Herzschallanalyse MTT Unterstützung Adaptiver Filter Und Neuronaler Netze, Technisches Messen 62, 1995, 3, pp. 107-112 w/English language summary.
D. Popivanov et al.,Detection of Successive Changes in Dynamics of EEG Time Series: Linear and Nonlinear Approach, 18thAnnual International Conference of the IEEE Engineering in Medicine and Biology Society, 1996, pp. 1590-1591.
J. Pardey et al.,A Review of Parametric Modelling Techniques for EEG Analysis, Med. Eng. Phys., vol. 18, No. 1, pp. 2-11, 1996.
J. Kohlmorgen, et al.,Analysis of Wake/Sleep EEG with Competing Experts, pp. 1077-1082, Aug. 10, 1997.
N. H. Packard et al.,Geometry from a Time Series, Physical Review Letters, vol. 45, No. 9, Sep. 1980, pp. 712-716.
Lawrence R. Rabiner, Fellow, IEEE,A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Readings In Speech Recognition, Eds. Alex Waibel et al., San Mateo, Morgan Kaufmann, 1990, pp. 267-296.
John Hertz et al.,Introduction to the Theory of Neural Computation, Addition-Wesley Publishing Company, 1991, Chapter 9 “Unsupervised Competitive Learning”, p. 1-10.
G. Pfurtscheller et al.,Sleep Classification in Infants Based on Artificial Neural Networks, Biomedizinische Technik, Band 37, Heft Jun. 1992, pp. 112-130.
Andreas Draeger et al.,Prādiktive Regelung Verfahrenstechnischer Anlagen Nit Neuronalen Netzen, Automatisierungstechnische Praxis 37, 1995, 4, pp. 55-61 w/English language summary.
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Stephan Ester,Herzschallanalyse MTT Unterstützung Adaptiver Filter Und Neuronaler Netze, Technisches Messen 62, 1995, 3, pp. 107-112 w/English language summary.

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