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Deriving Analytical Input–Output Relationship for Fuzzy Controllers Using Arbitrary Input Fuzzy Sets and Zadeh Fuzzy AND Operator

机译:使用任意输入模糊集和Zadeh Fuzzy AND运算符推导模糊控制器的分析性输入输出关系

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摘要

A fuzzy controller uses either Zadeh or product fuzzy AND operator, with the former being more frequently used than the latter. We have recently published a novel technique for deriving analytical input–output relation for the fuzzy controllers that use Zadeh AND operator and arbitrary trapezoidal input fuzzy sets, including triangular ones as special cases. In this paper, we have developed a general technique based on that technique to cover arbitrary types of input fuzzy sets. Moreover, we have established some necessary and sufficient conditions to characterize general relationship between shape of input fuzzy sets and shape of input space divisions, an important and integral issue because analytical relationship differs in different regions of input space. The new technique and the shape relations are applicable to any type of fuzzy controllers (e.g., Mamdani type or Takagi–Sugeno type). The analytical structures that we have derived provide an unprecedented opportunity to insightfully and rigorously examine the advantages and shortcomings of different design choices available for various components of the fuzzy controllers. We have focused on type selection for input fuzzy sets of Mamdani fuzzy controllers. Our preliminary analysis indicates that the fuzzy controllers using trapezoidal fuzzy sets may be understood (and possibly analyzed and designed) more sensibly and easily in the context of conventional control theory than the fuzzy controllers using any other types of fuzzy sets. Our proposition is that trapezoidal fuzzy sets should be the first choice and used most of time. Possible implication for automatic learning of input fuzzy sets via neural networks or genetic algorithms is briefly discussed.
机译:模糊控制器使用Zadeh或乘积模糊AND运算符,前者比后者更常用。最近,我们发布了一种新颖的技术,用于推导使用Zadeh AND算子和任意梯形输入模糊集(包括特例三角集)的模糊控制器的输入-输出关系。在本文中,我们开发了一种基于该技术的通用技术来覆盖任意类型的输入模糊集。此外,我们已经建立了一些必要和充分的条件来表征输入模糊集的形状与输入空间划分的形状之间的一般关系,这是一个重要而不可或缺的问题,因为分析关系在输入空间的不同区域有所不同。新技术和形状关系适用于任何类型的模糊控制器(例如,Mamdani型或Takagi–Sugeno型)。我们得出的分析结构提供了前所未有的机会,可以洞察和严格地检查模糊控制器各个组件可用的不同设计选择的优缺点。我们专注于Mamdani模糊控制器的输入模糊集的类型选择。我们的初步分析表明,与使用任何其他类型的模糊集的模糊控制器相比,在常规控制理论的背景下,使用梯形模糊集的模糊控制器可以更明智,更轻松地理解(并可能进行分析和设计)。我们的主张是梯形模糊集应该是首选,并且在大多数情况下都应使用。简要讨论了通过神经网络或遗传算法自动学习输入模糊集的可能含义。

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