In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

近年来，基于系统识别理论的模糊识别已成为一个热门的学术话题。区间2型模糊逻辑系统（IT2 FLS）已成为一种新兴技术。本文设计了一种基于Nagar-Bardini（NB）结构的单例IT2 FLS，用于模糊识别问题。选择IT2 FLS的主要隶属函数的先行条件作为具有不确定标准偏差的高斯类型2主要隶属函数。然后，使用反向传播算法根据推导链规则来调整IT2 FLS的参数。与第一类模糊逻辑系统相比，仿真研究表明，所提出的IT2 FLS具有更好的模糊识别问题泛化能力。