Patent ID: 7058617

Claim:
A prediction engine for predicting an operational value for a system based on measured operational values of the system, the prediction engine comprising: an operational value input for receiving a measured operational value; a prediction processor connected to said operational value input and having a mapping layer, and an operational value output connected to said prediction processor; wherein said prediction processor is trained by the steps of: providing a training data set having a set of training input data u(t) and target output data y(t) representative of the operation of a system, the training data existing over only a portion of the input space, training the prediction processor using the training input data and the target output data with a predetermined training algorithm to generate a trained model for making predictions, constraining the training algorithm during training of the model to maintain the sensitivity of the operational value at the operational value output with respect to the operational values input at the operational value input substantially within user defined gain constraint bounds by iteratively minimizing an objective function over substantially the input space as a function of a data objective and a constraint objective, wherein the data objective has a data fitting learning rate and the gain constraint objective has a constraint learning rate, and varying the data fitting learning rate and the gain constraint learning rate as a function of the data objective and the constraint objective after selective iterative steps.