Method for operating a neural network with missing and/or incomplete data
First Claim
1. A method for estimating error in a prediction output space of a predictive system model 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.
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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).
200 Citations
16 Claims

1. A method for estimating error in a prediction output space of a predictive system model 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.  View Dependent Claims (2, 3, 4)


5. A method for providing a measure of validity in a prediction output space of a predictive system model that provides a prediction output and operates over a prediction input space, comprising the steps of:

receiving an input vector comprising a plurality of input values that occupy the prediction input space; outputting a validity measure output 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 validity of the system model that is previously learned on a set of training data, the representation of the validity of this system model being a function of a distribution of the training data on the prediction input space that was input thereto during training to provide a measure of the validity of the prediction output of the prediction system model.  View Dependent Claims (6, 7, 8, 9)


10. 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; the predictive system model comprising a nonlinear 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 nonlinear model mapping the prediction input space to the prediction output space through a nonlinear 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.  View Dependent Claims (11, 12, 13, 14, 15, 16)

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