Computer system architecture for probabilistic modeling

• US 20060229854A1
• Filed: 07/29/2005
• 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
• Application Number:
  US11/192,360
• Publication Number:
  US 20060229854A1
• 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 system for probabilistic modeling includes a display and one or more input devices. A processor may be configured to execute instructions for generating at least one view representative of a probabilistic model and providing the at least one view to the display. The instructions may also include receiving data through the one or more input devices, running a simulation of the probabilistic model based on the data, and generating a model output including a predicted probability distribution associated with each of one or more output parameters of the probabilistic model. The model output may be provided to the display.

Claims

1. A computer system for probabilistic modeling, comprising:

a display;

one or more input devices; and

a processor configured to execute instructions for;

generating at least one view representative of a probabilistic model;

providing the at least one view to the display;

receiving data through the one or more input devices;

running a simulation of the probabilistic model based on the data;

generating a model output including a predicted probability distribution associated with each of one or more output parameters of the probabilistic model; and

providing the model output to the display.

2. The computer system of claim 1, wherein the data includes a probability distribution associated with at least one input parameter to the probabilistic model.

3. The computer system of claim 1, wherein the probabilistic model represents interrelationships between one or more input parameters to the probabilistic model and the one or more output parameters, and the predicted probability distribution associated with the one or more output parameters represents a probability of compliance with a desired set of model requirements.

4. The computer system of claim 1, wherein the processor is further configured to execute instructions for:

obtaining information relating to actual values for the one or more output parameters;

determining whether a divergence exists between the actual values and the predicted probability distribution associated with the one or more output parameters; and

issuing a notification if the divergence is beyond a predetermined threshold.

5. The computer system of claim 1, wherein the at least one view is included on the display in a browser window.

6. The computer system of claim 1, wherein the model output is included on the display in a browser window.

7. A computer system for building a probabilistic model, comprising:

at least one database;

a display; and

a processor configured to execute instructions for;

obtaining, from the at least one database, data records relating to one or more input variables and one or more output parameters;

selecting one or more input parameters from the one or more input variables;

generating, based on the data records, the probabilistic model indicative of interrelationships between the one or more input parameters and the one or more output parameters, wherein the probabilistic model is configured to generate statistical distributions for the one or more input parameters and the one or more output parameters, based on a set of model constraints; and

displaying at least one view to the display representative of the probabilistic model.

8. The computer system of claim 7, wherein the at least one view is included in a browser window.

9. The computer system of claim 7, including:

at least one input device, and

wherein the processor is further configured to execute instructions for;

receiving data through the at least one input device;

running a simulation of the probabilistic model based on the data;

generating a model output including a predicted probability distribution associated with each of the one or more output parameters; and

providing the model output to the display.

10. The computer system of claim 7, wherein the processor is further configured to execute instructions for:

constructing the at least one view based on a selected version of the probabilistic model and one or more object-based information elements selected for inclusion in the at least one view.

11. The computer system of claim 7, wherein the selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the one or more input parameters from the one or more input variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

12. The computer system of claim 7, wherein generating the probabilistic model includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

13. The computer system of claim 7, wherein the probabilistic model is configured to generate to generate the statistical distributions by:

determining a candidate set of input parameters with a maximum zeta statistic using a genetic algorithm; and

determining the statistical distributions of the one or more input parameters based on the candidate set,

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 of the computational model.

14. A computer readable medium including instructions for:

displaying at least one view representative of a probabilistic model, wherein the probabilistic model is configured to represent interrelationships between one or more input parameters and one or more output parameters and to generate statistical distributions for the one or more input parameters and the one or more output parameters, based on a set of model constraints;

receiving data through at least one input device;

running a simulation of the probabilistic model based on the data;

generating a model output including a predicted probability distribution associated with each of the one or more output parameters; and

providing the model output to a display.

15. The computer readable medium of claim 14, wherein the model output is included in a browser window.

16. The computer readable medium of claim 14, further including instructions for building the probabilistic model including:

obtaining, from at least one database, data records relating to one or more input variables and one or more output parameters;

selecting the one or more input parameters from the one or more input variables; and

generating the probabilistic model based on the data records.

17. The computer readable medium of claim 16, wherein the generating includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

18. The computer readable medium of claim 16, wherein the selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the one or more input parameters from the one or more input variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

19. The computer readable medium of claim 14, wherein the probabilistic model is configured to generate the statistical distributions by:

determining a candidate set of input parameters with a maximum zeta statistic using a genetic algorithm; and

determining the statistical distributions of the one or more input parameters based on the candidate set,

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 of the computational model.

20. The computer readable medium of claim 14, further including instructions for:

constructing the at least one view based on a selected version of the probabilistic model and one or more object based information elements selected for inclusion in the at least one view.

21. The computer readable medium of claim 14, further including instructions for

obtaining information relating to actual values for the one or more output parameters;

determining whether a divergence exists between the actual values and the predicted probability distribution associated with the one or more output parameters; and

issuing a notification if the divergence is beyond a predetermined threshold.