As an emerging technology, interval type-2 fuzzy logic systems (IT2 FLSs) have drawn great attentions in the past decade years. However, the computational intensive and time consuming type-reduction (TR) block may hinder the real applications IT2 FLSs. Unlike the most popular Karnik–Mendel (KM) iterative algorithms, the noniterative algorithms decrease the computational cost greatly. The comparison between the discrete and continuous algorithms is still an open problem. This paper compares the sum operations in discrete noniterative algorithms and the integral operations in continuous noniterative algorithms, and discovers the inner relations between the discrete and continuous noniterative algorithms. Then, three types of noniterative algorithms are adopted for performing the centroid TR of IT2 FLSs. Three computer simulations show the calculation results of sampling based discrete noniterative algorithms can accurately approximate the corresponding continuous noniterative algorithms as varying the number of sampling of primary variable appropriately, in addition, the computational efficiencies of former are much higher than the latter, which provide the potential value for designing IT2 FLSs.

作为一种新兴技术，区间类型2模糊逻辑系统（IT2 FLS）在过去十年中引起了极大的关注。但是，计算量大且耗时的类型减少（TR）块可能会阻碍实际的应用程序IT2 FLS。与最流行的Karnik-Mendel（KM）迭代算法不同，非迭代算法大大降低了计算成本。离散算法和连续算法之间的比较仍然是一个未解决的问题。本文比较了离散非迭代算法的求和运算与连续非迭代算法的积分运算，发现了离散与连续非迭代算法之间的内在联系。然后，采用三种类型的非迭代算法来执行IT2 FLS的质心TR。