Accurate on-line support vector regression incorporated with compensated prior knowledge

When the training data required by the data-driven model is insufficient or difficult to cover the sample space completely, incorporating the prior knowledge and prior knowledge compensation module into the support vector regression (PESVR) can significantly improve the accuracy and generalization performance of the model. However, the optimization problem to be solved is very complex, resulting long training time, and it must be retrained all the data from scratch every time the training set is modified. Comparing to standard support vector regression (SVR), PESVR has multiple input datasets and more complex objective function and constraints, including several coupling constraints, the existing methods cannot effectively solve accurate on-line learning of this nested (i.e. fully coupled) model. In this paper, an accurate on-line support vector regression incorporated with prior knowledge and error compensation is proposed. Under the constraint of Karush–Kuhn–Tucker conditions, the model parameters are updated recursively through the sequential adiabatic incremental adjustments. The error compensation model and the prediction model are updated simultaneously when a real measured sample or prior knowledge sample is added to or removed from the training set. The updated model is identical to the model produced by the batch learning algorithm. Experiments on an artificial dataset and several benchmark datasets show encouraged results for online learning and prediction.