【摘要】 广义二型模糊逻辑系统在近年来成为学术研究的热点问题,而降型是该系统中的核心模块。最近的研究证明了连续Nie-Tan(CNT)算法是计算区间二型模糊集质心的准确方法。发现了离散Nie-Tan(NT)算法中的求和运算和CNT算法中的求积分运算的内在联系,用2类算法完成基于广义二型模糊集α-平面表达理论的广义二型模糊逻辑系统质心降型。3个计算机仿真实验表明,当适当增加主变量采样点个数时,所提出的基于主变量采样的离散NT算法计算出的广义二型模糊逻辑系统质心降型集和解模糊化值结果可以精确地逼近基准的CNT算法,且采样离散NT算法的计算效率远远高于CNT算法的效率。更多还原

【Abstract】 The generalized type-2 fuzzy logic system has become a hot academic research issue in recent years, and the reduced type is the core module of the system. Recent studies have proved that the continuous Nie-Tan(CNT) algorithm is an accurate method to calculate the centroid of the interval type-2 fuzzy set. This paper discovers the internal connection between the summation operation in the discrete Nie-Tan(NT) algorithm and the integration operation in the CNT algorithm, and adopts two types of algorithms to perform the centroid type-reduction of generalized type-2 fuzzy logic systems based on the alpha-planes representation theory of general type-2 fuzzy sets. Three computer simulation experiments prove that, when the number of sampling points of the main variable is appropriately increased, the centroid reduced set and defuzzified value of the generalized type-2 fuzzy logic system calculated by the proposed discrete NT algorithm based on the main variable sampling can be accurately close to the benchmark CNT algorithm, and the computational efficiency of the sampling discrete NT algorithm is much higher than that of the CNT algorithm.更多还原