Method and arrangement for modeling a system

• US 20030115016A1
• Filed: 10/22/2002
• Published: 06/19/2003
• Est. Priority Date: 02/28/2000
• Status: Active Grant

• Current Assignee: Panoratio Database Images GmbH (Panoratio Holdings, Inc.)
• Original Assignee: Panoratio Database Images GmbH (Panoratio Holdings, Inc.)
• Inventors: Tresp, Volker, Hofmann, Reimar, Haft, Michael
• Granted Patent: US 7,072,795 B2
• Time in Patent Office: Days
• US Class Current: 702/179
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]

Abstract

The invention relates to a method and an arrangement for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least one of said state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by means of said sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Claims

1. Method for the determination of at least one partial model of a model of a system, a state of the system being described by state quantities,

in which plural sets of values of the state quantities are determined,

in which a probability distribution for the state quantities is determined from the sets,

in which the partial model of the system is determined using the sets and the probability distribution for the state quantities and using a statistical learning method, the partial model describing the system under the precondition of the probability distribution for the state quantities,

wherein

at least one of the state quantities is a discrete state quantity.

2. Method according to claim 1,

wherein the model includes plural partial models.

3. Method according to claim 2,

wherein all the state quantities are discrete state quantities.

4. Method according to one of claims 1-3,

wherein the probability distribution is a multinomial distribution or a binomial distribution.

5. Method according to claims 1-4,

wherein at least one characteristic set of values of the state quantities is determined, the probability distribution for the state quantities having an extremum for at least one characteristic set.

6. Method according to claim 5

wherein the extremum is a maximum.

7. Method according to claim 5 or 6,

wherein a semantic designation is determined for the characteristic set.

8. Method according to one of claims 5-7,

wherein plural characteristic sets are determined.

9. Method according to one of claims 1-8,

wherein the at least one discrete state quantity is determined by the discretization of a continuous state quantity.

10. Method according to one of claims 1-9,

wherein a measure of a statistical uncertainty is determined for a partial model.

11. Method according to claim 10,

wherein the measure of the statistical uncertainty is taken into account in the statistical learning method.

12. Method according to claim 11,

wherein the measure of the statistical uncertainty is taken into account such that only one such partial model is learned which has a small measure of the statistical uncertainty.

13. Method according to one of claims 10-12,

wherein the measure of the statistical uncertainty is a conditional entropy.

14. Method according to one of claims 1-13,

wherein the model is a Bayesian network.

15. Method according to one of claims 1-14,

wherein a logarithmic likelihood is determined in the statistical learning method.

16. Method according to one of claims 1-15,

wherein an expectation maximization method is carried out in the statistical learning method.

17. Method according to one of claims 1-16,

wherein a state of the system is determined by means of a measurement of state quantities and/or a presetting of state quantities.

18. Method according to one of claims 1-17,

used for a prognosis of a state of a system.

19. Method according to claim 18, wherein only partial state quantities of a set of state quantities are determined in the prognosis, and further state quantities of this set are determined using the model and the partial determined state quantities.

20. Method according to one of claims 1-19,

wherein the system is defined as an Internet access.

21. Method according to claim 20,

used for an analysis of a plurality of Internet accesses.

22. Arrangement for the determination of at least one partial model of a model of a system, a state of the system being described by state quantities, with a processor which is arranged such that

plural sets of values of the state quantities can be determined,

a probability distribution for the state quantities can be determined from the sets,

the partial model of the system can be determined using the sets and the probability distribution for the state quantities and using the statistical learning method, the partial model describing the system under the precondition of the probability distribution for the state quantities,

wherein

at least one of the state quantities is a discrete state quantity.