A Fuzzy Ensemble Method With Deep Learning for Multi-Robot System

Liao, Peng; Xu, Mg; Yang, Congying

doi:10.1109/access.2020.3042439

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…Likewise, in another study (Savchenko et al 2018 ), the authors implemented a fuzzy analysis with a CNN in still-to-video recognition. Moreover, the model has also been implemented in human action recognition and robotics (Bendre et al 2020 ; Chen et al 2020a , b ; Liao et al 2020 ; Mohmed et al 2020 ; Wu et al 2020 ).…”

Section: Analysis and Synthesis Of Datamentioning

confidence: 99%

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

et al. 2022

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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.

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