Deriving prediction intervals for neuro-fuzzy networks

In this paper, we describe a method to derive prediction intervals for neuro-fuzzy networks used as predictive systems. The method also enables the definition of prediction intervals for the fuzzy rules that constitute the rule base of the neuro-fuzzy network, resulting in a more readable and robust knowledge base. Moreover, the method does not depend on a specific architecture and can be applied to a variety of neuro-fuzzy models. An illustrative example and a real-world case study are reported to show the effectiveness of the proposed method.