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Effect of different initializations on EKM algorithm

机译:不同初始化对EKM算法的影响

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As an integral part of interval type-2 fuzzy logic system (IT2FLS), type reduction (TR) plays a vital role in determining the performance of IT2FLS. Out of many type reduction algorithms, only Karnik-Mendel type TR algorithms capture the essence of interval type-2 fuzzy sets in type reduction. Enhanced Karnik-Mendel (EKM) algorithm is the most commonly used TR algorithm. In this work, we propose three new initializations for EKM algorithm. It is shown they are performing better than EKM and one of the proposed initializations significantly outperforms others. The performance gain can be upto 40% as per comprehensive simulation results demonstrated in this paper. Our findings are justified by computational time savings and iteration requirement for switch point search.
机译:作为间隔2型模糊逻辑系统(IT2FLS)的组成部分,类型减少(TR)在确定IT2FLS的性能方面起着至关重要的作用。在许多类型归约算法中,只有Karnik-Mendel类型TR算法在类型归约中捕获了间隔2型模糊集的本质。增强的Karnik-Mendel(EKM)算法是最常用的TR算法。在这项工作中,我们为EKM算法提出了三种新的初始化方法。结果表明,它们的性能优于EKM,并且其中一种建议的初始化方法明显优于其他方法。根据本文演示的综合仿真结果,性能提升可以达到40%。我们的发现被节省的计算时间和切换点搜索的迭代需求所证明。

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