Broad Learning Based Dynamic Fuzzy Inference System With Adaptive Structure and Interpretable Fuzzy Rules
Broad Learning Based Dynamic Fuzzy Inference System With Adaptive Structure and Interpretable Fuzzy Rules
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具有自适应结构和可解释模糊规则的基于广泛学习的动态模糊推理系统
DOI：
10.1109/tfuzz.2021.3112222
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发表时间：
2022
影响因子：
11.9
通讯作者：
Wenyu Zhang
中科院分区：
文献类型：
--
作者：
Kaiyuan Bai;Xiaomin Zhu;S. Wen;Runtong Zhang;Wenyu Zhang
本文探讨了应用广义学习系统（BLS）实现一种新的Takagi-Sugeno-Kang （TSK）神经模糊模型，即基于广义学习的动态模糊推理系统（BL-DFIS）的可行性。它不仅提高了神经模糊模型的准确性和可解释性，而且解决了模型不能自主确定最优结构的难题。BL-DFIS首先在BLS框架下实现了一个TSK模糊系统，其中利用极限学习机自编码器快速解析地获取特征表示，并在增强节点中集成可解释的语言模糊规则，保证了系统的高可解释性。同时，设计了扩展增强单元来实现一阶TSK模糊系统。此外，针对BL-DFIS的学习，提出了一种带有内部剪枝和更新机制的动态增量学习算法，使系统能够自动组装最优结构，从而获得紧凑的规则库和优异的分类性能。在基准数据集上的实验表明，所提出的BL-DFIS在使用最精简的模型结构的同时，比一些最先进的非模糊和神经模糊方法具有更好的分类性能。
This article investigates the feasibility of applying the broad learning system (BLS) to realize a novel Takagi–Sugeno–Kang (TSK) neuro-fuzzy model, namely a broad learning based dynamic fuzzy inference system (BL-DFIS). It not only improves the accuracy and interpretability of neuro-fuzzy models but also solves the challenging problem that models are incapable of determining the optimal architecture autonomously. BL-DFIS first accomplishes a TSK fuzzy system under the framework of BLS, in which an extreme learning machine auto-encoder is employed to obtain feature representation in a fast and analytical way, and an interpretable linguistic fuzzy rule is integrated into the enhancement node to ensure the high interpretability of the system. Meanwhile, the extended-enhancement unit is designed to achieve the first-order TSK fuzzy system. In addition, a dynamic incremental learning algorithm with internal pruning and updating mechanism is developed for the learning of BL-DFIS, which enables the system to automatically assemble the optimal structure to obtain a compact rule base and an excellent classification performance. Experiments on benchmark datasets demonstrate that the proposed BL-DFIS can achieve a better classification performance than some state-of-the-art nonfuzzy and neuro-fuzzy methods, simultaneously using the most parsimonious model structure.