Neuro-Fuzzy Synergy: Intuitionistic Fuzzy-Based Learning for Smarter Decisions

Authors

  • John Robinson P * Department of Mathematics, PG & Research, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli, 620017, Tamil Nadu, INDIA. https://orcid.org/0009-0007-1477-6378

https://doi.org/10.22105/masi.v2i3.73

Abstract

This paper introduces a novel Artificial Neural Network (ANN)-based Decision Support System (DSS) model that leverages intuitionistic fuzzy matrix information, aggregated using a newly proposed Hamming distance-influenced operator. The study explores the application of these innovative approaches and operators in solving Multiple Attribute Group Decision Making (MAGDM) problems under intuitionistic fuzzy environments. Key aspects of aggregation operators and ANNs, particularly the Back Propagation method, are rigorously examined to demonstrate their utility in this context. A groundbreaking aggregation operator is presented to effectively handle intuitionistic fuzzy data, providing a robust foundation for decision-making scenarios. To validate the proposed framework, a numerical example is analyzed and resolved using the ANN Back Propagation approach, showcasing improved accuracy and reduced Bias. The findings highlight the superiority of the proposed method over conventional ANN approaches in tackling MAGDM problems, paving the way for more effective and intelligent decision-making solutions.

Keywords:

Multiple attribute group decision making, Artificial neural network, Aggregation operators, Back Propagation, Intuitionistic fuzzy sets

References

  1. [1] Atanassov, K., Sotirov, S., & Pencheva, T. (2023). Intuitionistic fuzzy deep neural network. Mathematics, 11(3), 716. https://doi.org/10.3390/math11030716
  2. [2] Hájek, P., & Olej, V. (2015). Intuitionistic fuzzy neural network: The case of credit scoring using text information. In Engineering applications of neural networks: 16th international conference, EANN 2015, Rhodes, Greece. (pp. 337–346). Springer. https://doi.org/10.1007/978-3-319-23983-5_31
  3. [3] Jekova, I., Christov, I., & Krasteva, V. (2022). Atrioventricular synchronization for detection of atrial fibrillation and flutter in one to twelve ECG leads using a dense neural network classifier. Sensors, 22(16), 6071. https://doi.org/10.3390/s22166071
  4. [4] Krasteva, V., Christov, I., Naydenov, S., Stoyanov, T., & Jekova, I. (2021). Application of dense neural networks for detection of atrial fibrillation and ranking of augmented ECG feature set. Sensors, 21(20), 6848. https://doi.org/10.3390/s21206848
  5. [5] Petkov, T., Bureva, V., & Popov, S. (2021). Intuitionistic fuzzy evaluation of an artificial neural network model. Notes on intuitionistic fuzzy sets, 27(4), 71–77. https://B2n.ir/xb2017f
  6. [6] Robinson, P. J., & Leonishiya, A. (2023). Application of varieties of learning rules in an intuitionistic fuzzy artificial neural network. International conference on machine intelligence for research & innovations (pp. 35–45). Springer. https://doi.org/10.1007/978-981-99-8129-8_4
  7. [7] Sotirov, S., & Atanassov, K. (2009). Intuitionistic fuzzy feed-forward neural network. Cybernetics and information technologies, 9(2), 71–76. https://cit.iict.bas.bg/CIT_09/v9-2/62-68.pdf
  8. [8] Leonishiya, A., & Robinson, J. (2023). Varieties of linguistic intuitionistic fuzzy distance measures for linguistic intuitionistic fuzzy TOPSIS method. Indian journal of science and technology, 16(33), 2653–2662. https://doi.org/10.17485/IJST/v16i33.640-icrsms
  9. [9] Leonishiya, A., & Robinson, P. J. (2023). A fully linguistic intuitionistic fuzzy artificial neural network model for decision support systems. Indian journal of science and technology, 16, 29–36. https://doi.org/10.17485/IJST/v16iSP4.ICAMS136
  10. [10] Xu, Z., & Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International journal of general systems, 35(4), 417–433. https://doi.org/10.1080/03081070600574353
  11. [11] Yager, R. R., & Filev, D. P. (1999). Induced ordered weighted averaging operators. IEEE transactions on systems, man, and cybernetics, part b (Cybernetics), 29(2), 141–150. https://doi.org/10.1109/3477.752789
  12. [12] Yager, R. R. (2001). The power average operator. IEEE transactions on systems, man, and cybernetics-part a: Systems and humans, 31(6), 724–731. https://doi.org/10.1109/3468.983429
  13. [13] Kumar, A., Sharma, T. K., & Verma, O. P. (2023). Detection of heart failure by using machine learning. International conference on machine intelligence for research & innovations (pp. 195–203). Springer. https://doi.org/10.1007/978-981-99-8135-9_17
  14. [14] Sharma, R., Verma, O. P., & Kumari, P. (2023). Application of dragonfly algorithm-based interval type-2 fuzzy logic closed-loop control system to regulate the mean arterial blood pressure. International conference on machine intelligence for research & innovations (pp. 183–193). Springer.

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Published

2025-07-11

Issue

Vol. 2 No. 3 (2025): Management Analytics and Social Insights

Section

Articles

How to Cite

Robinson P, J. . (2025). Neuro-Fuzzy Synergy: Intuitionistic Fuzzy-Based Learning for Smarter Decisions. Management Analytics and Social Insights, 2(3), 172-181. https://doi.org/10.22105/masi.v2i3.73

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