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Maclaurin series expansion complexity-reduced center of sets type-reduction + defuzzification for interval type-2 fuzzy systems

机译:区间2型模糊系统的Maclaurin级数展开式降低复杂度集减少+反模糊化

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This paper provides a mathematical analysis that shows how the crisp output of an IT2 FLS that is obtained by using the Begian-Melek-Mendel (BMM) formula compares to the one obtained by using center-of-sets type-reduction followed by defuzzification (COS TR + D). This is made possible by reformulating the structural solutions of the two optimization problems that are associated with COS TR, and then expanding each of them using a Maclaurin series expansion. As a result of doing this, we show that BMM is the zero-order approximation to COS TR + D. Additionally, by retaining the zero-order and first-order terms from the Maclaurin series expansions, we provide a new Enhanced BMM, one that is non-iterative, has a closed form and is much faster than using the EKM algorithms for COS TR. Although the Enhanced BMM formula is slower than BMM, we demonstrate, by means of extensive simulations, that it is from 5% to 50% more accurate than is BMM for achieving the same numerical solution that is obtained from COS TR + D; and, it is at least 94% faster than when EKM is used for COS TR +D, which makes the Extended BMM a very strong candidate for use in real time applications of IT2 FLSs.
机译:本文提供了一种数学分析,该数学分析显示了使用Begian-Melek-Mendel(BMM)公式获得的IT2 FLS的清晰输出与使用集中心类型缩减再进行去模糊化获得的输出之间的比较( COS TR + D)。通过重新构造与COS TR相关的两个优化问题的结构解,然后使用Maclaurin级数展开对它们中的每一个进行扩展,使之成为可能。这样做的结果是,我们证明BMM是COS TR + D的零阶近似。此外,通过保留Maclaurin系列扩展中的零阶和一阶项,我们提供了一个新的增强型BMM,即它是非迭代的,具有封闭形式,并且比将EKM算法用于COS TR要快得多。尽管增强型BMM公式比BMM慢,但通过广泛的仿真,我们证明,与获得与COS TR + D相同的数值解的BMM相比,它的精度比BMM高出5%至50%。并且,与将EKM用于COS TR + D的速度相比,它至少快94%,这使得扩展BMM成为IT2 FLS实时应用中非常强大的候选者。

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