Towards comparing adaptive type-2 input based non-singleton type-2 FLS and non-singleton FLSs employing Gaussian inputs
Towards comparing adaptive type-2 input based non-singleton type-2 FLS and non-singleton FLSs employing Gaussian inputs
复制标题
比较基于自适应 2 类输入的非单例 2 类 FLS 和采用高斯输入的非单例 FLS
DOI：
10.1109/fuzz-ieee.2012.6251255
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
H. Hagras
中科院分区：
文献类型：
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作者：
Nazanin Sahab;H. Hagras
模糊逻辑系统（fls）被认为提供了非常好的性能，能够处理现实世界环境和应用中的不确定性和不精确性。与1型fls相比，使用2型fls可以处理更高级别的不确定性。大多数的二型模糊语义分析采用单型二型模糊语义分析，通过模糊集表示前因模糊集中的语言标签来处理遇到的输入不确定性。然而，单例2型fls假设输入信号是完美的，因此没有处理输入信号中的不确定性的规定。因此，有一些研究非单例2型FLS的努力。然而，采用非单态2型FLSs的论文假设模糊输入具有预定义的形状（主要是高斯），这可能无法正确地模拟所遇到的不确定性。在我们之前的工作中，我们提出了基于非单态2型FLS的自适应2型输入，它采用不假设任何特定形状的动态输入。我们已经展示了基于非单例2型FLS的自适应2型输入如何优于单例（1型和2型）FLS。在本文中，我们将比较基于自适应2型输入的非单态2型FLS与其他采用高斯模糊输入的非单态（1型和2型）FLS。我们将展示真实世界的机器人实验，展示在遇到大量不确定性时，基于自适应2型输入的非单例2型FLS如何优于采用高斯模糊输入的非单例FLS。
Fuzzy logic Systems (FLSs) are credited with providing very good performances which are able to handle the uncertainty and imprecision present in real-world environments and applications. Using type-2 FLSs can enable handling higher levels of uncertainty when compared to type-1 FLSs. The majority of the type-2 FLSs employ singleton type-2 FLSs which handle the encountered input uncertainty through fuzzy sets representing the linguistic labels in the antecedent fuzzy sets. However, singleton type-2 FLSs assume that the input signal is perfect and thus there is no provision for handling the uncertainties in the incoming input signals. Hence, there have been some efforts to investigate non-singleton type-2 FLS. However, the papers that employed non-singleton type-2 FLSs assumed that the fuzzy inputs are having a predefined shape (mostly Gaussian) which might not model the encountered uncertainty properly. In our previous works, we presented adaptive type-2 input based non-singleton type-2 FLS which employs dynamic inputs which are not assuming any specific shape. We have shown how the adaptive type-2 input based non-singleton type-2 FLS outperforms singleton (type-1 and type-2) FLSs. In this paper, we will compare the adaptive type-2 input based non-singleton type-2 FLS with other non-singleton (type-1 and type-2) FLSs which employ Gaussian fuzzy inputs. We will present real-world robot experiments showing how the adaptive type-2 input based non-singleton type-2 FLS outperforms the non-singleton FLSs which employ Gaussian fuzzy inputs when large amounts of uncertainty are encountered.