ELM-based control system applications: A bibliometric analysis and review

Enggar Banifa Pratiwi; Prawito Prajitno; Edi Kurniawan; Enggar Banifa Pratiwi; Prawito Prajitno; Edi Kurniawan

doi:10.55981/j.mev.2024.889

ELM-based control system applications: A bibliometric analysis and review

Enggar Banifa Pratiwi, Prawito Prajitno, Edi Kurniawan

Abstract

This study conducts a bibliometric analysis of the extreme learning machine (ELM) research, with a particular emphasis on ELM-based control systems and applications. The objective of this study is to identify research trends, collaboration opportunities, and challenges in ELM applications. The analysis comprises the identification and retrieval of 3,174 articles from Scopus between 2018 and 2023. VOSviewer 1.6.20 is used for data interpretation, identifying six distinct keyword clusters and revealing both well-established research areas and emerging fields with significant potential for future exploration. Key research trends indicate a shift towards advanced or hybrid approaches, with recent interest in integrating optimization techniques. In the analysis, opportunities for collaboration with leading researchers are also highlighted. The findings emphasize the wide range of applications for ELM in improving the robustness of control systems while also highlighting important issues that need to be addressed. Finally, this study provides valuable insights into the current state and future directions of ELM research, especially ELM-based control systems.

10.55981/j.mev.2024.889

Keywords

extreme learning machine; control system; bibliometric analysis; ELM application

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