The construction of fuzzy logic systems (FLSs) using data-driven techniques has become the most popular modeling approach. However, this approach still faces critical challenges, including the difficulty in obtaining concise models for high-dimensional data and generating accurate fuzzy rules to simulate human inference mechanism. To tackle these issues, a new FLS modeling framework called data-driven elastic FLS (DD-EFLS) is proposed in this paper. The DD-EFLS has two key characteristics. First, the fuzzy rules in the rule base can use different feature subspaces that are extracted from the original high-dimensional space to yield simple and accurate rules in feature spaces of lower dimensionality. Second, fuzzy inferences from various views are implemented by embedding different rules in the corresponding subspaces to imitate human inference mechanism. Based on the DD-EFLS framework, an elastic Takagi–Sugeno–Kang (TSK) FLS modeling method (ETSK-FLS) is proposed to train the elastic TSK FLS using the concise rules and a more human-like inference mechanism for modeling tasks based on high-dimensional datasets. The characteristics and advantages of the proposed framework and the ETSK-FLS method are validated experimentally using both synthetic and real-world datasets.

中文翻译：

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数据驱动的弹性模糊逻辑系统建模：构建具有类人推理机制的简洁系统

使用数据驱动技术构建模糊逻辑系统 (FLS) 已成为最流行的建模方法。然而，这种方法仍然面临着严峻的挑战，包括难以获得高维数据的简洁模型和生成准确的模糊规则来模拟人类推理机制。为了解决这些问题，本文提出了一种新的 FLS 建模框架，称为数据驱动弹性 FLS（DD-EFLS）。DD-EFLS 有两个关键特性。首先，规则库中的模糊规则可以使用从原始高维空间中提取的不同特征子空间，在低维特征空间中产生简单而准确的规则。第二，通过在相应的子空间中嵌入不同的规则来模仿人类的推理机制来实现来自各种视图的模糊推理。基于 DD-EFLS 框架，提出了一种弹性 Takagi-Sugeno-Kang (TSK) FLS 建模方法 (ETSK-FLS) 来训练弹性 TSK FLS，使用简洁的规则和更像人的推理机制来训练基于建模任务的模型。在高维数据集上。所提出的框架和 ETSK-FLS 方法的特征和优势通过使用合成和现实世界数据集的实验验证。