Uniform Design-Based Interval Type-2 Neuro-fuzzy System and Its Performance Verification

Abstract

In this paper, an interval type-2 neuro-fuzzy system with uniform design-based rule generation approach is proposed, and Begian–Melek–Mendel method is used for defuzzification. To the consequent learning, two least squares methods are involved in the consequent design for the interval type-2 neuro-fuzzy system, one is the recursive singular value decomposition, and the other is the weighted least squares estimator. Besides the interval type-2 neuro-fuzzy system modelling, another aim of this paper is to verify the interval type-2 neuro-fuzzy systems’ performance from the point of view of statistics, not just the average prediction accuracy or the best results from hundreds of iteration. With this in mind, a distribution-free least squares estimation method is used to assess the interval type-2 neuro-fuzzy system’s modelling capability.