In this article, a novel design of a hierarchicalfuzzy system (HFS) based on a self-organized fuzzy partition and fuzzy autoencoder is proposed. The initial rule set of the system is empty, and all the fuzzy sets and fuzzy rules are generated by a self-organized fuzzy partition algorithm. By adopting an improved box plot data standardization method, the processed data can more accurately represent the distribution characteristics of the input data, which improve the accuracy and the rationality. A fuzzy autoencoder is used to train the HFS layer by layer, which can not only ensure the effectiveness of the fuzzy system's hidden layer variables but also provide interpretability. Compared with the traditional fuzzy logic system, the HFS reduces the total number of rules and the complexity. The proposed HFS is tested on three different regression datasets. The experimental results illustrate that the hierarchical self-organized fuzzy system still performs better in terms of regression accuracy indicators than the self-organized fuzzy system.

中文翻译：

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基于模糊自编码器的分层模糊逻辑系统自组织方法


在本文中，提出了一种基于自组织模糊划分和模糊自动编码器的分层模糊系统（HFS）的新颖设计。系统初始规则集为空，所有模糊集和模糊规则均由自组织模糊划分算法生成。采用改进的箱线图数据标准化方法，处理后的数据能够更准确地表征输入数据的分布特征，提高了准确性和合理性。采用模糊自编码器逐层训练HFS，不仅可以保证模糊系统隐含层变量的有效性，而且可以提供可解释性。与传统的模糊逻辑系统相比，HFS减少了规则总数和复杂度。所提出的 HFS 在三个不同的回归数据集上进行了测试。实验结果表明，层次自组织模糊系统在回归精度指标方面仍然比自组织模糊系统表现得更好。