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Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey.

Noureen Talpur¹, Said Jadid Abdulkadir¹, Hitham Alhussian¹

¹Centre for Research in Data Science (CeRDaS), Computer Information Science Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.

Artificial Intelligence Review

|April 18, 2022

Summary

Deep neuro-fuzzy systems (DNFS) combine deep neural networks and fuzzy logic for interpretable AI. This review maps 105 studies from 2015-2020, revealing growing research and applications.

Keywords:

Big data Classification systems Deep neural network Deep neuro-fuzzy systems Fuzzy systems Optimization methods

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Intelligence

Background:

Deep neural networks (DNNs) offer powerful data processing but lack transparency.
The 'black-box' nature of DNNs motivates hybrid approaches for enhanced interpretability.
Deep neuro-fuzzy systems (DNFS) emerge as a solution, integrating DNNs with fuzzy logic.

Purpose of the Study:

To conduct a systematic review of Deep Neuro-Fuzzy Systems (DNFS) research.
To analyze progress, trends, challenges, and future scope in the DNFS domain.
To provide a comprehensive overview of DNFS implementation and application.

Main Methods:

Systematic literature review using a study mapping process.
Search conducted across five scientific directories for publications from 2015-2020.
Analysis of 105 identified studies to address specific research questions.

Main Results:

Identified and analyzed 105 DNFS studies published between 2015 and 2020.
Mapped the concept, optimization methods, research intensity, and application domains of DNFS.
Confirmed a rapidly growing research trend in DNFS with significant future potential.

Conclusions:

DNFS research is expanding rapidly, offering high interpretability and accuracy.
The review highlights key trends, research gaps, and challenges in the DNFS field.
Provides guidance for future research, fostering advancements in DNFS techniques and applications.