Ensuring monotonic gain characteristics in estimated models by fuzzy modelstructures

We consider the situation where a non-linear physical system is identified from input-output data. In case no specific physical structural knowledge a bout the system is available, parameterized grey-box models cannot be used. Identification in black-box type of model structures is then the only alte rnative, and general approaches like:neural:nets, neuro-fuzzy models, etc., have to be applied. However, certain non-structural knowledge about the sy stem is sometimes available. It could be known, e.g., that the step respons e is monotonic, or that the steady-state gain curve is monotonic. The main question is then how to utilize and maintain such information in an otherwi se black-box framework. In this paper we show how this can be done, by appl ying a specific fuzzy model structure, with strict parametric constraints. The usefulness of the approach is illustrated by experiments on real-world data. (C) 1999 Elsevier Science Ltd. All rights reserved.