Method and apparatus for operating a neural network with missing and/or incomplete data
First Claim
1. A network for estimating the error in the prediction output space of a predictive system model operating over a prediction input space to then control the output of the predictive system model, 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; and
a controller for modifying the output of the predictive system model as a function of said output error prediction vector.
5 Assignments
0 Petitions
Accused Products
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).
49 Citations
20 Claims
-
1. A network for estimating the error in the prediction output space of a predictive system model operating over a prediction input space to then control the output of the predictive system model, 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; and
a controller for modifying the output of the predictive system model as a function of said output error prediction vector. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A network for providing a measure of the validity in the prediction output space of a predictive system model that provides a prediction output and operates over a prediction input space to then control the output of the predictive system model, comprising:
-
an input for receiving an input vector comprising a plurality of input values that occupy the prediction input space;
an output for outputting a validity measure output 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 validity of the system model that was learned on a set of training data, the representation of the validity of the system model being a function of the distribution of the training data in the prediction input space that was input thereto during training to provide a measure of the validity of the system model prediction output; and
a controller for modifying the output of the predictive system model as a function of said validity measure output vector. - View Dependent Claims (11, 12, 13, 14, 15, 16)
-
-
17. A method for estimating the error in the prediction output space of a predictive system model over a prediction input space to then control the output of the predictive system model, 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; and
a controller for modifying the output of the predictive system model as a function of the output error prediction vector. - View Dependent Claims (18, 19, 20)
-
Specification