Combining neural and conventional paradigms for modeling, prediction, and control

Promising research using neural networks for modeling, prediction, and control, exploits the complementarity of the two paradigms to address realistic problem situations. This paper develops a general framework for identifying the possible ways of combining neural networks with physical models, model-based estimators, and conventional controllers. The framework presented not only naturally leads to the previously proposed schemes in the literature, but also reveals several new possibilities.