| 摘要 |
Method and device for modelling the variables relevant to a process as a function of other parameters describing or influencing the process, termed regressors, by means of multiple regression for the purpose of process identification, monitoring, analysis and control or regulation. The classical method of stepwise multiple regression is expanded by the introduction of the so-called collinearity cone into a recursive method yielding all "best" collinearity-free regression models. The method is completed by giving consideration to the regression errors and by restriction to the absolutely necessary matrix elements. Stable regression models of various sizes are thus produced with little expenditure of time. Further, either linear or nonlinear regression functions permit a more accurate process analysis or modelling. By automatic learning in the case of newly occurring combinations of regressive values, it is also possible to apply the process to process monitoring, control and regulation.
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