A Constraint-based Framework for Incorporating A Priori Knowledge into Fuzzy Modelling

Incorporation of various sources of a priori knowledge into data-driven fuzzy modelling is an important task. But a major problem with current approaches is that they are mostly problem-specific and lacking an effective framework to bring different sources of knowledge into the task of modelling. In this paper, we propose a constraint-based framework for the incorporation of a priori knowledge into data-driven-based fuzzy modelling. We first investigate a logical taxonomy of background knowledge in learning a fuzzy model. Then, based on this taxonomy, we can develop a framework for incorporating prior knowledge into a constraint-based fuzzy modelling. Finally, two simulation examples, a nonlinear function fitting problem and a dynamic time series prediction problem, are provided for the embodiment of the proposed idea.