Black-box models from input-output measurements

A black-box model of a system is one that does not use any particular prior knowledge of the character or physics of the relationships involved. It is therefore more a question of “curve-fitting” than “modeling”. In this presentation several examples of such black-box model structures will be given. Both linear and non-linear structures are treated. Relationships between linear models, fuzzy models, neural networks and classical non-parametric models are discussed. Some reasons for the usefulness of these model types are also given. Ways to fit black box structures to measured input-output data are described, as well as the more fundamental (statistical) properties of the resulting models