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Adaptive non-singleton type-2 fuzzy logic systems: A way forward for handling numerical uncertainties in real world applications

机译:自适应非单类型2型模糊逻辑系统:处理现实应用中数值不确定性的一种方法

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摘要

Real world environments are characterized by high levels of lin-guistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision avail-able in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applica-tions such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first gen-eration of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncer-tainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numer-ical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents' linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties. © 2006-2011 by CCC Publications.
机译:现实环境中的语言和数字不确定性很高。模糊逻辑系统(FLS)被认为是处理现实环境和应用程序中存在的不确定性和不精确性的适当方法。自从模糊逻辑发明以来,它已成功应用于许多实际应用中,例如洗衣机,食品加工机,电池充电器,电动汽车以及其他几种家用和工业设备。 FLS的第一代是使用1型模糊集的1型FLS。后来发现使用2型FLS可以处理更高级别的不确定性。最近的工作表明,在包含高不确定性水平的应用中,间隔2型FLS可以胜过1型FLS。但是,大多数区间2型FLS使用单例区间2型FLS来处理语言和输入数值不确定性,这些区间混合了将仅由语言标签2型模糊集处理的数值和语言不确定性。这忽略了以下事实:如果存在输入数字不确定性,则它们会影响FLS的输入输入。即使在使用非单一2型FLS的论文中,也假定输入信号具有预定义的形状(主要是高斯或三角形),该形状可能无法反映出实际不确定性分布,该不确定性分布可能随相关测量而变化。在本文中,我们将提出一种基于自适应非单间隔2型FLS的新方法,其中数值不确定性将由非单类型2模糊输入建模和处理,而语言不确定性将由由区间2型模糊集处理以表示前项的语言标签。非单类型2型模糊输入是动态的,它们是根据数据自动生成的,对于与给定传感器关联的分布,它们没有采取特定的形状。我们将使用一个真实世界的机器人展示一些真实世界的实验,这些实验将展示当遇到高水平的不确定性时,拟议的2型非单身2型FLS会比单身1型和2型同类产品产生更好的性能。 ©2006-2011,CCC出版物。

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    Sahab, N; Hagras, H;

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  • 年度 2011
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  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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