University of Tabriz Computational Methods for Differential Equations 2345-3982 14 1 2026 11 01 A dynamic neuro-fuzzy approach for pattern classification 337 347 19446 10.22034/cmde.2025.65211.2984 EN Erfan Veisi Continuous Improvement Department, Mirab Valves Company, Tehran, Iran. Bahram Sadeghi Bigham Department of Computer Science, Faculty of Mathematical Sciences, Alzahra University, Tehran, Iran. Mahdi Vasighi Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences, Zanjan, Iran. Journal Article 2024 12 29 Nowadays, the application of neuro-fuzzy methods has been discovered more than ever for pattern recognition. These powerful tools are able to model the reality of data structure as it should be because, in the real world, datasets are defined in a fuzzy concept. In this research, we present a novel neuro-fuzzy method called Fuzzy Growing Map (FGM), combining the dynamic properties of the Growing Self-Organizing Map (GSOM) and fuzzy set theory. FGM is a dynamic neural fuzzy inference system based on if-then rules, which has the ability to generate fuzzy rules based on certain criteria during the learning phase. This approach can be used as a classifier and approximator. In addition, the trained FGM was used to visualize the fuzzy sets as a map, and the structure of the data can easily be revealed in the feature space. To investigate the effectiveness of FGM, several benchmark datasets were analyzed, and the experimental results for classification show improvements in terms of accuracy and topographic error compared to classification algorithms Fuzzy Self-Organizing Map (FSOM)and Counter Propagation Neural Networks (CPNN). https://cmde.tabrizu.ac.ir/article_19446_bfc81baa57d552b7161bed5a93289e5d.pdf