초록 ▼

A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, th

A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state models for each output. The dynamic model can be identified utilizing a weighting factor for the gain to force the dynamic gain of the dynamic model to the steady-state gain by weighting the difference thereof during optimization of the dynamic model. The steady-state model is optimized utilizing gain constraints during the optimization procedure such that the gain of the network is prevented from exceeding the gain constraints.

대표청구항 ▼

[ What is claimed is:] [1.] A method for training a steady-state non-linear model of a plant, comprising the steps of:generating an input set of historical training data representative of the historical operation of the plant and having input data u(t) and output data y(t);identifying a linear dynam

[ What is claimed is:] [1.] A method for training a steady-state non-linear model of a plant, comprising the steps of:generating an input set of historical training data representative of the historical operation of the plant and having input data u(t) and output data y(t);identifying a linear dynamic model of the plant representing the dynamics of the plant for a given output variable of the plant;pre-filtering the historical input data through the dynamic model to impress the dynamics of the plant as defined by the linear dynamic model on the input data u(t); andtraining a non-linear model of the plant with the pre-filtered input data u(t) to provide a stored representation of the steady-state operation in the non-linear model associated with the given output variable.