Online Adaptation of Correlation and Regression Models

Adaptive algorithms for data-based models are often of fundamental importance in order to identify real-time processes which possess a time-variant behaviour that would make a time-invariant model too inaccurate. Beyond that, an insufficiency of amount, distribution and/or quality of actual recorded measurement data can occur, such that the model cannot meet the expectations at a particular time. In this case, the incorporation of new recorded data into previously generated models can improve the model's accuracy and reduce the bias or model error captured due to original noisy data. In this paper algorithms and strategies for adapting a special kind of data-based models, namely so-called correlation and regression models, are demonstrated