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TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy

机译：TaSe，基于泰勒级数的模糊系统模型，结合了可解释性和准确性

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Typically, Takagi-Sugeno-Kang (TSK) fuzzy rules have been used as a powerful tool for function approximation problems, since they have the capability of explaining complex relations among variables using rule consequents that are functions of the input variables. But they present the great drawback of the lack of interpretability, which makes them not to be so suitable for a wide range of problems where interpretability of the obtained model is a fundamental key. In this paper, we present a novel approach that extends the work by Bikdash (IEEE Trans. Fuzzy Systems 7 (6) (1999) 686-696), in order to obtain an interpretable and accurate model for function approximation from a set of I/O data samples, which make use of the Taylor Series Expansion of a function around a point to approximate the function using a low number of rules. Our approach also provides an automatic methodology for obtaining the optimum structure of our Taylor series-based (TaSe) fuzzy system as well as its pseudo-optimal rule-parameters (both antecedents and consequents).
机译：通常，Takagi-Sugeno-Kang（TSK）模糊规则已用作函数逼近问题的强大工具，因为它们具有使用作为输入变量的函数的规则结果来解释变量之间复杂关系的能力。但是它们呈现出缺乏可解释性的巨大缺点，这使得它们不适合于解决其中以所获得模型的可解释性为基本关键的广泛问题。在本文中，我们提出了一种新颖的方法，该方法扩展了Bikdash的工作（IEEE Trans.Fuzzy Systems 7（6）（1999）686-696），以便从一组I中获得可解释且准确的函数逼近模型。 / O数据样本，该样本利用函数在点周围的泰勒级数展开来使用少量规则来近似函数。我们的方法还提供了一种自动方法，可用于获取基于泰勒级数（TaSe）的模糊系统的最优结构及其伪最佳规则参数（既有条件又有结果）。

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