Interval type-2 fuzzy logic systems (IT2 FLSs) have been widely used in many areas. Among which, type-reduction (TR) is an important block for theoretical study. Noniterative algorithms do not involve the complicated iteration process and obtain the system output directly. By discovering the inner relations between discrete and continuous noniterative algorithms, this paper proposes three types of weighted-based noniterative according to the Newton-Cotes quadrature formulas in numerical integration techniques. Moreover, the continuous noniterative algorithms are considered as the benchmarks for computing. Four simulation experiments are provided to illustrate the performances of weighted-based noniterative algorithms for computing the defuzzified values of IT2 FLSs. Compared with the original noniterative algorithms, the proposed weighted-based algorithms can obtain smaller absolute errors and faster convergence speeds under the same sampling rate, which afford the potential values for designing T2 FLSs.

中文翻译：

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区间2型模糊逻辑系统的质心归约的基于加权的非迭代算法研究

区间2型模糊逻辑系统（IT2 FLS）已广泛应用于许多领域。其中，类型减少（TR）是理论研究的重要组成部分。非迭代算法不涉及复杂的迭代过程，而是直接获取系统输出。通过发现离散和连续非迭代算法之间的内在联系，本文根据数值积分技术中的牛顿-科茨正交公式，提出了三种基于加权的非迭代算法。此外，连续的非迭代算法被视为计算的基准。提供了四个仿真实验，以说明基于加权的非迭代算法在计算IT2 FLS的去模糊值时的性能。与原始的非迭代算法相比，