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.

本文提出了一种嵌入到深度学习领域的计算高效的模糊逻辑系统（FLS）学习方法，通过提供计算高效的 FLS 实现，最小化训练时间并利用深度学习框架中提供的小批量优化器和自动微分机制，解决了 FLS 学习的挑战，并在基准数据集上展示了深度学习框架在 FLS 中的高效性。