초록 ▼

In the control of a material-processing process in a regulated system, a preliminary adjustment of the system takes place at the beginning of each process cycle as a function of a precalculated process parameter. A material characteristic which is relevant for the process and which in turn is depend

In the control of a material-processing process in a regulated system, a preliminary adjustment of the system takes place at the beginning of each process cycle as a function of a precalculated process parameter. A material characteristic which is relevant for the process and which in turn is dependent on state variables (such as the composition of the material and its temperature), is included in an advance calculation of the process parameter. The relationship between the state variables and the material property is modelled in a neural network which forms a prediction value for the material property on its output side. As a function of the deviation between the prediction value and an actual value for the material property which is determined based on measuring the process parameter during the process cycle, an adaptive change of the network parameters takes place in the sense of reducing this deviation.

대표청구항 ▼

[ What is claimed is:] [1.] A method for controlling the processing of a material in a regulated system having at least one process cycle, the method comprising:performing a preliminary adjustment of the regulated system at a beginning of each process cycle of the material-processing process as a fu

[ What is claimed is:] [1.] A method for controlling the processing of a material in a regulated system having at least one process cycle, the method comprising:performing a preliminary adjustment of the regulated system at a beginning of each process cycle of the material-processing process as a function of a precalculated process parameter, wherein precalculation of said process parameter includes a material property that is relevant to said material-processing process and is also dependent on a plurality of state variables of said material;applying said plurality of state variables to a neural network prior to the beginning of the process cycle, said neural network including variable network parameters;forming in said neural network a prediction value for said material property at an output of said neural network;measuring said process parameter during said process cycle;determining an actual value of said material property after said process cycle by recalculation;adaptively changing said variable network parameters as a function of a deviation between said prediction value and the actual value of the material property in order to reduce said deviation.