Interval Type 2 Neuro-Fuzzy Systems Based on Interval Consequents

Abstract

There are several ways to synthesize fuzzy systems and neural networks. The so-called neuro-fuzzy systems exhibit advantages of both techniques, namely learning abilities of neural networks and natural language description of fuzzy systems. Recently the concept of type 2 fuzzy sets, i.e. fuzzy sets with fuzzy membership grades, was introduced to fuzzy inference systems. This paper presents a new neuro-fuzzy system of type 2 derived under the assumption that the rule antecedents are characterized by interval fuzzy membership grades and the consequents are intervals. An application for the checking of the driver's steering behaviors is given as an example.