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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers
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Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers

机译：通过语言修饰语的正则化提取具有可解释子模型的Takagi-Sugeno模糊规则

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摘要

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases.
机译：为了保持TS子模型的可理解性，本文提出了一种从数据中构建高木-Sugeno（TS）模糊系统的方法，并提出了语言修饰符来表征模糊集。所提出的语言修饰符的一个良好特性是，它们可以在从数据识别模糊系统的过程中，拓宽模糊集的核心，同时缩小相邻隶属函数（MF）的重叠。结果，通过自动匹配相应子区域中的全局模型（GM），识别出的TS子模型往往会主导系统行为，从而在产生可区分的输入空间分区的同时，提供了良好的TS模型可解释性。但是，GM准确性和模型可解释性是两个相互矛盾的建模目标，提高模糊模型的可解释性通常会降低模糊模型的GM性能，反之亦然。因此，一个具有挑战性的问题是如何构建不仅具有良好的全局性能而且具有良好的子模型可解释性的TS模糊模型。为了在GM性能和子模型可解释性之间取得良好的折衷，提出了一种正则化学习算法，其中GM目标函数与根据已识别MF的模糊性扩展指标定义的局部模型目标函数结合在一起。此外，通过采用QR分解方法选择重要的模糊规则并减少冗余规则，从而获得了简化的规则库。实验研究表明，所提出的方法所识别的TS模型具有良好的子模型可解释性和令人满意的GM性能，并且具有简约的规则库。

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