Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning
This work addresses the training complexity and curse of dimensionality for researchers and practitioners using FLSs, but it is incremental as it integrates existing DL techniques.
The paper tackled the learning challenges of Fuzzy Logic Systems (FLS) for large-scale data by presenting a computationally efficient method embedded in Deep Learning, which minimized training time and leveraged DL optimizers and automatic differentiation on benchmark datasets.
Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure. Yet, in learning large-scale data challenges arise, such as the curse of dimensionality and training complexity of FLSs. The complexity is due mainly to the constraints to be satisfied as the learnable parameters define FSs and the complexity of the center of the sets calculation method, especially of IT2-FLSs. This paper explicitly focuses on the learning problem of FLSs and presents a computationally efficient learning method embedded within the realm of Deep Learning (DL). The proposed method tackles the learning challenges of FLSs by presenting computationally efficient implementations of FLSs, thereby minimizing training time while leveraging mini-batched DL optimizers and automatic differentiation provided within the DL frameworks. We illustrate the efficiency of the DL framework for FLSs on benchmark datasets.