Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning

TL;DR

This paper introduces a deep learning-based approach to efficiently train large-scale fuzzy logic systems, addressing computational challenges and reducing training time using mini-batch optimization and automatic differentiation.

Contribution

It presents a novel, computationally efficient learning method for FLSs integrated with deep learning frameworks, suitable for large-scale data.

Findings

Demonstrates reduced training time on benchmark datasets

Shows improved scalability for large-scale FLSs

Validates effectiveness of DL-based FLS learning approach

Abstract

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.