Pruning for interpretability of large spanned eTS

J. V. Ramos, A. Dourado
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引用次数: 5

Abstract

On-line implementation of mechanisms for merging membership functions and rule base simplification are studied in order to improve the interpretability of the eTS fuzzy models. This allows the minimization of redundancy and complexity of the models that may arrive during its development, increasing transparency (human interpretability). The on-line learning technique used is the evolving first-order Takagi-Sugeno (eTS) fuzzy models with rule spanned. A four rule fuzzy system is obtained for the Auto-Mpg benchmark data set with acceptable accuracy
大跨度et的可解释性修剪
为了提高eTS模糊模型的可解释性,研究了在线实现的隶属函数合并机制和规则库简化机制。这使得在开发过程中可能出现的模型的冗余和复杂性最小化,增加了透明度(人类可解释性)。使用的在线学习技术是演化的一阶Takagi-Sugeno (eTS)模糊模型。对Auto-Mpg基准数据集得到了一个精度可接受的四规则模糊系统
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