A dynamic neuro-fuzzy approach for pattern classification

Document Type : Research Paper

Authors

1 Continuous Improvement Department, Mirab Valves Company, Tehran, Iran.

2 Department of Computer Science, Faculty of Mathematical Sciences, Alzahra University, Tehran, Iran.

3 Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.

Abstract

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).

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Computational Methods for Differential Equations
Volume 14, Issue 1
January 2026
Pages 337-347

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History

  • Receive Date: 29 December 2024
  • Revise Date: 03 February 2025
  • Accept Date: 02 March 2025

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