Method and apparatus for operating neural network with missing and/or incomplete data

• US 5,613,041 A
• Filed: 09/20/1995
• Issued: 03/18/1997
• Est. Priority Date: 11/24/1992
• Status: Expired due to Term

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Pavilion Technologies Incorporated (Rockwell Automation, Inc.)
• Inventors: Ferguson, Ralph B., Keeler, James D., Hartman, Eric J.
• Primary Examiner(s): MacDonald, Allen R.
• Assistant Examiner(s): Dorvil, Richemond
• Application Number:
  US08/531,100
• Time in Patent Office: 545 Days
• Field of Search: 395/11, 395/23, 395/24
• US Class Current: 706/17
• CPC Class Codes: G06F 17/17 Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method G06F17/18 takes precedence ; interpolation for numerical control G05B19/18
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs

Abstract

A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22). Additionally, a validity model (16) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (12). This predicts the confidence in the predicted output which is also input to the decision processor (20). The decision processor (20) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (10) can be utilized to train the system model (12).

Claims

1. A network for estimating error in a prediction output space of a predictive system model operating over a prediction input space as a prediction error, comprising:

an input for receiving an input vector comprising a plurality of input values that occupy the prediction input space;

an output for outputting an output prediction error vector that occupies an output space corresponding to the prediction output space of the predictive system model;

a processing layer for mapping the prediction input space to the prediction output space through a representation of the prediction error in the predictive system model to provide said output prediction error vector;

a preprocess input for receiving an unprocessed data input vector having associated therewith unprocessed data, said unprocessed data input vector associated with substantially the same input space as said input vector, said unprocessed data input vector having errors associated with the associated unprocessed data in select portions of the prediction input space; and

a data preprocessor for processing the unprocessed data in the unprocessed data input vector to minimize the errors therein to provide said input vector on an output.

2. The network of claim 1, wherein said unprocessed data input vector is comprised of data having portions thereof that are unusable and said data preprocessor comprises a reconciliation device for reconciling the unprocessed data to replace the portions thereof that are unusable with reconciled data.

3. The network of claim 1, wherein said data preprocessor is operable to calculate and output an uncertainty for each value output by said data preprocessor.

4. The network of claim 1, wherein the predictive system model comprises a non-linear model having an input for receiving the input vector that is within the prediction input space and an output for outputting a predicted output vector within a prediction output space, said non-linear model mapping the prediction input space to the prediction output space through a non-linear representation of a system.

5. The network of claim 4, wherein the predictive system model is trained on a set of training data having uncertainties associated therewith that give rise to a prediction error in the set of training data and wherein said processing layer is operable to map the prediction input space to the prediction output space through a representation of the combined prediction error in the predictive system model and the prediction error in the set of training due to the uncertainties in the set of training data.

6. The network of claim 4 and further comprising:

a plurality of decision thresholds for defining predetermined threshold values for said output prediction error vector;

an output control for effecting a change in the value of said predicted output vector from the predictive system model; and

a decision processor for receiving said output prediction error vector and comparing said predicted output vector to said decision thresholds and operating said output control to effect said change on the value of said predicted output vector when the value of said output prediction error vector meets a predetermined relationship with respect to said decision thresholds.

7. The network of claim 5, wherein said non-linear representation is a trained representation that is trained on a finite set of input data within the input space in accordance with a predetermined training algorithm and further comprising a validity model for providing a representation of a validity of the predicted output vector of the predictive system model for a given value of the input vector within the input space, said validity model comprising:

an input for receiving the input vector within the input space;

an output for outputting a validity output vector corresponding to the output space;

a validity processor for generating said validity output vector in response to input of said input vector, the value of said validity output vector corresponding to the amount of training data on which the predictive system model was trained in the region of the input space proximate the value of the input vector.

8. The network of claim 7, and further comprising:

a plurality of decision thresholds for defining predetermined threshold values for said validity output vector;

an output control for effecting a change in the value of said predicted output vector from the predictive system model; and

a decision processor for receiving said validity output vector and comparing said validity output vector to said decision thresholds, and operating said output control to effect said change in the value of said predicted output vector when the value of said validity output vector meets a predetermined relationship with respect to said decision thresholds.