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Novel Methods for the Design of General Type-2 fuzzy Sets based on Device Characteristics and Linguistic Labels Surveys

机译:基于器件特征和语言标签调查的通用类型-2模糊组设计新方法

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Fuzzy Logic Systems are widely recognized to be successful at modelling uncertainty in a large variety of applications. While recently interval type-2 fuzzy logic has been credited for the ability to better deal with large amounts of uncertainty, general type- 2 fuzzy logic has been a steadily growing research area. All fuzzy logic systems require the accurate specification of the membership functions' (MFs) parameters. While some work for automatic or manual design of these parameters has been proposed for type-1 and interval type-2 fuzzy logic, the problem has not yet been widely addressed for general type-2 fuzzy logic. In this paper we propose two methods which allow the automatic design of general type-2 MFs using either data gathered through a survey on the linguistic variables required or, in the case of physical devices (e.g. sensors, actuators), using data directly gathered from the specific devices. As such, the proposed methods allow for the creation of general type-2 MFs which directly model the uncertainty incorporated in the respective applications. Additionally, we demonstrate how interval type-2, type-1 and the recently introduced zSlices based general type- 2 MFs can be extracted from the automatically designed general type-2 MFs. We also present a recursive algorithm that computes the convex approximation of generated fuzzy sets.
机译:模糊逻辑系统被广泛认识到成功在各种应用中建模不确定性。虽然最近间隔-2型模糊逻辑已被记入能够更好地处理大量不确定性,但是一般类型的模糊逻辑一直是一个稳步增长的研究区。所有模糊逻辑系统都需要准确规范隶属函数'(MFS)参数。虽然已经提出了用于1型和间隔类型-2模糊逻辑的自动或手动设计的一些工作,但该问题尚未广泛寻找一般类型-2模糊逻辑。在本文中,我们提出了两种方法,该方法允许使用通过在物理设备(例如传感器,执行器)的语言变量的调查中收集的任何数据自动设计一般类型2 MFS的方法。使用直接收集的数据特定设备。因此,所提出的方法允许创建一般类型-2 MFS,其直接模拟包含在各个应用中的不确定性。此外,我们展示了Type-2 Type-2,1型基于类型的ZSLices的一般类型-2MFS,可以从自动设计的一般类型-2MFS中提取。我们还提出了一种递归算法,其计算产生的模糊集的凸起近似。

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