초록 ▼

A tool, and the method of making the tool, for process system identification that is based on the general purpose learning capabilities of neural networks. The tool and method can be used for a wide variety of system identification problems with little or no analytic effort. A neural network is trai

A tool, and the method of making the tool, for process system identification that is based on the general purpose learning capabilities of neural networks. The tool and method can be used for a wide variety of system identification problems with little or no analytic effort. A neural network is trained using a process model to approximate a function which relates process input and output data to process parameter values. Once trained, the network can be used as a system identification tool. In principle, this approach can be used for linear or nonlinear processes, for open or closed loop identification, and for identifying any or all process parameters.

대표청구항 ▼

[ It is claimed:] [1.] A method for developing a tool for identifying at least one parameter of a process--wherein said parameter is characterized as a constant in an equation defining a mathematical model of said process--;said tool capable of providing a signal representative of values of said at

[ It is claimed:] [1.] A method for developing a tool for identifying at least one parameter of a process--wherein said parameter is characterized as a constant in an equation defining a mathematical model of said process--;said tool capable of providing a signal representative of values of said at least one parameter which is recognized by a controller of said process, said method comprising the steps of:determining ranges for each of said at least one parameter;modeling said equation via a computer program;utilizing said program to generate a set of training examples, each of said examples having (1) selected values of said at least one parameter from within said respective ranges and (2) process model output data resulting from said program when a process model input is provided to said process model for said selected values of said at least one parameter; andtraining an artificial neural network on said set of training examples by: (1) providing an input to said network comprising for each of said training examples respective values of at least said process model output data; and (2) the output of said network comprises respectively for each of said training examples estimates of at least one of said selected values of said at least one parameter, said signal being in a form recognizable by said controller as an indicator of a present value of a process model parameter so that said controller effectuates changes to the process based on such input.