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

• US 6,169,980 B1
• Filed: 10/06/1998
• Issued: 01/02/2001
• 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 Bruce, Hartman, Eric Jon, Keeler, James David
• Primary Examiner(s): Black, Thomas G.
• Assistant Examiner(s): Shah, Sanjiv
• Application Number:
  US09/167,400
• Time in Patent Office: 819 Days
• Field of Search: 706/17, 706/25, 706/52, 706/21
• US Class Current: 706/21
• 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 method for estimating error in a prediction output space of a predictive system model of a system over a prediction input space as a prediction error, comprising the steps of:

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

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

mapping the prediction input space to the prediction output space through a representation of the prediction error in the predictive system model to provide the output prediction error vector in the step of outputting;

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

processing the unprocessed data in the unprocessed data vector to minimize the errors therein to provide the input vector on an output;

controlling the system with a control network; and

modifying the control network as a function of the prediction error vector in accordance with a predetermined decision algorithm.

2. The method of claim 1, wherein the step of receiving an unprocessed data input vector comprises receiving an unprocessed data input vector that is comprised of data having portions thereof that are unusable and the step of processing the unprocessed data comprises reconciling the unprocessed data to replace the unusable portions thereof that are unusable with reconciled data.

3. A network for estimating error in a prediction output space of a predictive system model of a system 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;

the predictive system model comprising 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;

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; and

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;

controlling the system with a control network; and

modifying the control network as a function of the prediction error vector in accordance with a predetermined decision algorithm.

4. The network of claim 3, and further comprising:

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.