Method and device for conducting a process in a controlled system with at least one precomputed process parameter determined using a mathematical model having variable model parameters adjusted based on a network response of a neural network

Claim 1

A method for controlling a process with a controlling means and a computing means, the computing means having a mathematical model of the process, the mathematical model having at least one variable process model parameter, and with a neural network having variable network parameters, the method comprising steps of: a) supplying input variables to the computing means; b) precomputing, before each process run, at least one selected process parameter based on the input variables supplied to the computing means; c) presetting the at least one process parameter; d) measuring input values and process parameters during a process run, wherein the process run is controlled by the controlling means; e) supplying the measured input values and process parameters to the mathematical model; f) adaptively improving the precomputed process parameters based on the measured input values and process parameters, the step of adaptively improving the precomputed process parameters including sub-steps of: i) supplying input values that influence the at least one variable process model parameter to the neural network after the process run, ii) providing a value for the at least one variable process model parameter as a network response with the neural network, iii) comparing the precomputed at least one process parameter with the measured process parameters to form a deviation, and iv) adapting the network parameters of the neural network such that the deviation is reduced.

Claim 2

A method for controlling a process with a controlling means and a computing means, the computing means having a mathematical model of the process, the mathematical model having at least one variable process model parameter, and with a neural network having variable network parameters, the method comprising steps of: a) supplying input variables to the computing means; b) precomputing, before each process run, at least one selected process parameter based on the input variables supplied to the computing means; c) presetting the at least one process parameter; d) measuring input values and process parameters during a process run, wherein the process run is controlled by the controlling means; e) supplying the measured input values and process parameters to the mathematical model; f) adaptively improving the precomputed process parameters based on the measured input values and process parameters, the step of adaptively improving the precomputed process parameters including sub-steps of: i) supplying input values that influence the at least one variable process model parameter to the neural network after the process run, ii) providing a value for the at least one variable process model parameter as a network response with the neural network, iii) comparing the precomputed at least one process parameter with the measured process parameters to form a deviation, and iv) adapting the network parameters of the neural network such that the deviation is reduced.