Zeta statistic process method and system

• US 20060229852A1
• Filed: 04/08/2005
• Published: 10/12/2006
• Est. Priority Date: 04/08/2005
• Status: Abandoned Application

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Grichnik, Anthony J., Seskin, Michael, Bhasin, Vijaya
• Application Number:
  US11/101,554
• Publication Number:
  US 20060229852A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

A computer-implemented method is provided for model optimization. The method may include obtaining respective distribution descriptions of a plurality of input parameters to a model and specifying respective search ranges for the plurality of input parameters. The method may also include simulating the model to determine a desired set of input parameters based on a zeta statistic of the model and determining respective desired distributions of the input parameters based on the desired set of input parameters.

Claims

1. A computer-implemented method for model optimization, comprising:

obtaining respective distribution descriptions of a plurality of input parameters to a model;

specifying respective search ranges for the plurality of input parameters;

simulating the model to determine a desired set of input parameters based on a zeta statistic of the model; and

determining respective desired distributions of the input parameters based on the desired set of input parameters.

2. The computer-implemented method according to claim 1, wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\stackrel{j}{\underset{1}{\sum <br><br>}}<br><br>\stackrel{i}{\underset{1}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (x)}_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input.

3. The computer-implemented method according to claim 1, further including:

displaying graphs of the desired distributions of the input parameters.

4. The computer-implemented method according to claim 1, further including:

outputting the desired distributions of the input parameters.

5. The computer-implemented method according to claim 1, wherein simulating includes:

starting a genetic algorithm;

generating a candidate set of input parameters;

providing the candidate set of input parameters to the model to generate one or more outputs;

obtaining output distributions based on the one or more outputs;

calculating respective compliance probabilities of the one or more outputs; and

calculating a zeta statistic of the model.

6. The computer-implemented method according to claim 5, further including:

determining a minimum compliant probability from the respective compliant probabilities of the one or more outputs.

7. The computer-implemented method according to claim 6, further including:

setting a goal function of the genetic algorithm to maximize a product of the zeta statistic and the minimum compliant probability, the goal function being set prior to starting the genetic algorithm.

8. The computer-implemented method according to claim 7, wherein the simulating further includes:

determining whether the genetic algorithm converges; and

identifying the candidate set of input parameters as the desired set of input parameters if the genetic algorithm converges.

9. The computer-implemented method according to claim 8, further including:

choosing a different candidate set of input parameters if the genetic algorithm does not converge; and

repeating the step of simulating to identify a desired set of input parameters based on the different candidate set of input parameters.

10. The computer-implemented method according to claim 8, further including:

identifying one or more input parameters having a impact on the outputs that is below a predetermined level.

11. A computer system, comprising:

a console;

at least one input device; and

a central processing unit (CPU) configured to;

obtain respective distribution descriptions of a plurality of input parameters to a model;

specify respective search ranges for the plurality of input parameters;

simulate the model to determine a desired set of input parameters based on a zeta statistic of the model; and

determine respective desired distributions of the input parameters based on the desired set of input parameters.

12. The computer system according to claim 11, wherein the CPU is configured to calculate zeta statistic ζ

;

$\mathrm{\zeta <br><br>}=\stackrel{j}{\underset{1}{\sum <br><br>}}<br><br>\stackrel{i}{\underset{1}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (x)}_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input.

13. The computer system according to claim 11, the CPU being further configured to:

display graphs of the desired distributions of the input parameters.

14. The computer system according to claim 11, wherein, to simulate the model, the CPU is configured to:

set a goal function of a genetic algorithm to maximize a product of the zeta statistic and a minimum compliant probability;

start the genetic algorithm;

generate a candidate set of input parameters;

provide the candidate set of input parameters to the model to generate one or more outputs; and

obtain output distributions based on the one or more outputs;

15. The computer system according to claim 14, the CPU being further configured to:

calculate respective compliance probabilities of the one or more outputs;

determine the minimum compliant probability from the respective compliance probabilities of the one or more outputs;

calculate the zeta statistic of the model; and

calculate a product of the zeta statistic and the minimum compliant probability.

16. The computer system according to claim 15, the CPU being further configured to:

determine whether the genetic algorithm converges; and

identify the candidate set of input parameters as the desired set of input parameters if the genetic algorithm converges.

17. The computer system according to claim 16, the CPU being further configured to:

choose a different candidate set of input parameters if the genetic algorithm does not converge; and

repeat the step of simulating to identify a desired set of input parameters based on the different candidate set of input parameters.

18. The computer system according to claim 16, the CPU being further configured to:

identify one or more input parameters not having significant impact on the outputs.

19. The computer system according to claim 11, further including:

one or more databases; and

one or more network interfaces.

20. A computer-readable medium for use on a computer system configured to perform a model optimization procedure, the computer-readable medium having computer-executable instructions for performing a method comprising:

obtaining distribution descriptions of a plurality of input parameters to a model;

specifying respective search ranges for the plurality of input parameters;

simulating the model to determine a desired set of input parameters based on a zeta statistic of the model; and

determining desired distributions of the input parameters based on the desired set of input parameters.

21. The computer-readable medium according to claim 20, wherein simulating includes:

setting a goal function of a genetic algorithm to maximize a product of the zeta statistic and a minimum compliant probability;

starting the genetic algorithm;

generating a candidate set of input parameters;

providing the candidate set of input parameters to the model to generate one or more outputs; and

obtaining output distributions based on the one or more outputs;

22. The computer-readable medium according to claim 21, wherein simulating further includes:

calculating respective compliant probabilities of the one or more outputs;

determining the minimum compliant probability from the respective compliance probabilities of the one or more outputs;

calculating the zeta statistic of the model; and

calculating the product of the zeta statistic and the minimum compliant probability.

23. The computer-readable medium according to claim 22, wherein simulating further includes:

determining whether the genetic algorithm converges; and

identifying the candidate set of input parameters as the desired set of input parameters if the genetic algorithm converges.

24. The computer-readable medium according to claim 23, wherein simulating further includes:

choosing a different candidate set of input parameters if the genetic algorithm does not converge; and

repeating the step of simulating to identify a desired set of input parameters based on the different candidate set of input parameters.

25. The computer-readable medium according to claim 23, wherein simulating further includes:

identifying one or more input parameters not having significant impact on the outputs.