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

Abstract

Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems classified as deep neuro-fuzzy systems (DNFS). Studies regarding the implementation of DNFS have rapidly increased in the domains of computing, healthcare, transportation, and finance with high interpretability and reasonable accuracy. However, relatively few survey studies have been found in the literature to provide a comprehensive insight into this domain. Therefore, this study aims to perform a systematic review to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope related to DNFS studies. A study mapping process was prepared to guide a systematic search for publications related to DNFS published between 2015 and 2020 using five established scientific directories. As a result, a total of 105 studies were identified and critically analyzed to address research questions with the objectives: (i) to understand the concept of DNFS; (ii) to find out DNFS optimization methods; (iii) to visualize the intensity of work carried out in DNFS domain; and (iv) to highlight DNFS application subjects and domains. We believe that this study provides up-to-date guidance for future research in the DNFS domain, allowing for more effective advancement in techniques and processes. The analysis made in this review proves that DNFS-based research is actively growing with a substantial implementation and application scope in the future.

Keywords: Big data; Classification systems; Deep neural network; Deep neuro-fuzzy systems; Fuzzy systems; Optimization methods.

Fig. 1

Revised study mapping process

Fig. 2

PRISMA flow chart for selection of the studies in the systematic literature survey

Fig. 3

Included studies based on the publication types

Fig. 4

Representation of DNFS by combining the advantages of fuzzy systems and a DNN

Fig. 5

Sequential DNFS: a fuzzy systems incorporated with a DNN and b a DNN incorporated with fuzzy systems

Fig. 6

Illustrates an example of the sequential DNFS

Fig. 7

Gaussian membership function

Fig. 8

Structural design for parallel or fused DNFS

Fig. 9

Illustrates an example of parallel/fusion DNFS

Fig. 10

Sigmoid activation function

Fig. 11

Structural design of cooperative DNFS: a fuzzy deep neural network and b deep neuro-fuzzy network

Fig. 12

Illustrated example of cooperative DNFS

Fig. 13

Flow of fuzzy Rocchio’s algorithm

Fig. 14

Overall distribution of the optimization methods used with DNFS

Fig. 15

Distribution of the DNFS optimization methods in scientific databases

Fig. 16

Trend of exact methods in the DNFS domain

Fig. 17

Trend of population-based (PB) metaheuristic methods in the DNFS domain

Fig. 18

Publications of DNFS year-wise

Fig. 19

Publications of DNFS directory-wise

Fig. 20

Intensity of DNFS-related publications for application subjects in the computing domain

Fig. 21

The intensity of publications in different application domains of DNFS

Fig. 22

Distribution of records found in each application domain