A bibliometric analysis of the last ten years of fuzzy min-max neural networks

Abstract

Neural networks have been widely used in many application areas such as power systems, weather forecasting, face recognition, behaviour analysis, fingerprint identification, healthcare, fault detection, flood monitoring system, and surveillance system, and navigation. However, classic neural network methods are insufficient for real-life applications. To tackle with this problem, using the learning capability of neural networks and deduction capability of fuzzy systems, neural networks and fuzzy sets are incorporated called as NeuroFuzzy. The Fuzzy min-max neural network (FMNN) is a special type of NeuroFuzzy. In this paper, a bibliometric analysis is conducted on FMNN literature. We consider the studies that are published in the last decade due to there is a jump in this field in the last 10 years. Social network analysis results show that Chee Peng Lim, is the most influential researcher in the network. The Neurocomputing is found to be the most influential journal, publishing 12% of all publications in this field. In addition, the International Conference on Computing, Communication, and Networking Technologies is the most influential conference on FMNN. The findings of this paper can draw a road map for researchers in the FMNNs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.