Advances in fuzzy logic and algebraic structures for industrial applications in control theory
*Sita YadavCorresponding authorsyadav@aitpune.edu.inDepartment of Computer Engineering Army Institute of TechnologyPune, Maharashtra, 411015, IndiaView full profile → , Sagar Ranesagarrane@aitpune.edu.inDepartment of Computer Engineering Army Institute of TechnologyPune, Maharashtra, 411015, IndiaView full profile → , Vanishree Pabalkarvanishree.p@sims.eduSymbiosis Institute of Management Studies Symbiosis International (Deemed) UniversityPune, Maharashtra, 411020, IndiaView full profile → , Smriti Sahusmritisahu13@gmail.comDepartment of Electronics and Telecommunication Ajeenkya D Y Patil School of EngineeringPune, Maharashtra, 412105, IndiaView full profile → , Radhika Kulkarniradhika.kulkarni@vit.eduDepartment of Computer Engineering Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Amruta Chitariamruta.chitari@dypic.inDepartment of Computer Engineering Ajeenkya D Y Patil School of EngineeringPune, Maharashtra, 412105, IndiaView full profile → , Trupti Kattetruptikatte@gmail.comDepartment of Electronics and Computer Engineering Pravara Rural Engineering CollegeLoni, Maharashtra, 413736, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 05 Nov 2024
- Published Online:
- 30 Aug 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2373
- Pages:
- 1981–1991
Abstract
Keywords
Subject Classifications
References
[1] A. Babaei, J. Parker, and P. Moshave, “Adaptive neuro-fuzzy inference system (ANFIS) integrated with genetic algorithm to optimize piezoelectric cantilever-oscillator-spring energy harvester: Verification with closed-form solution,” Computer Engineering and Physical Modeling, vol. 5, pp. 1–22 (2022).
[2] O. J. P. Nayagam and K. Prasanna, “Response surface methodology and adaptive neuro-fuzzy inference system for adsorption of reactive orange 16 by hydrochar,” Global Journal of Environmental Science and Management, vol. 9, pp. 373–388 (2023).
[3] M. I. S. Guerra, M. F. U. de Araújo, J. T. de Carvalho Neto, and R. G. Vieira, “Survey on adaptative neural fuzzy inference system (ANFIS) architecture applied to photovoltaic systems,” Energy Systems, vol. 15, pp. 505–541 (2024).
[4] J. I. Obianyo, R. C. Udeala, and G. U. Alaneme, “Application of neural networks and neuro-fuzzy models in construction scheduling,” Scientific Reports, vol. 13, pp. 8199 (2023).
[5] P. H. D. Nguyen and A. R. F. Fayek, “Applications of fuzzy hybrid techniques in construction engineering and management research,” Automation in Construction, vol. 134, pp. 104064 (2022).
[6] M.-C. Huang, “A sender-initiated fuzzy logic control method for network load balancing,” Journal of Computer and Communications, vol. 12, pp. 110–122 (2024).
[7] N. J. Zade, N. P. Lanke, B. S. Madan, N. P. Katariya, P. Ghutke, and P. Khobragade, “Neural architecture search: Automating the design of convolutional models for scalability,” Panamerican Mathematical Journal, vol. 34, no. 4, pp. 178–193 (Dec. 2024).
[8] S. N. Silva, M. A. S. d. S. Goldbarg, L. M. D. d. Silva, and M. A. C. Fernandes, “Application of fuzzy logic for horizontal scaling in Kubernetes environments within the context of edge computing,” Future Internet, vol. 16, pp. 316 (2024).
[9] A. Yazdinejad, A. Dehghantanha, R. M. Parizi, G. Srivastava, and H. Karimipour, “Secure intelligent fuzzy blockchain framework: Effective threat detection in IoT networks,” Computers in Industry, vol. 144, pp. 103801 (2023).
[10] M. Pérez-Gaspar, J. Gomez, E. Bárcenas, and F. Garcia, “A fuzzy description logic based IoT framework: Formal verification and end user programming,” PLoS ONE, vol. 19, pp. e0296655 (2024).
[11] R. Firouzia, R. Rahmania, and T. Kanter, “An autonomic IoT gateway for smart home using fuzzy logic reasoner,” Procedia Computer Science, vol. 177, pp. 102–111 (2020).
[12] M. Y. Aalsalem, “An intelligent adaptive neuro-fuzzy for solving the multipath congestion in Internet of Things,” Journal of Information Systems Engineering and Management, vol. 8, pp. 23845 (2023).
[13] S. L. Jany Shabu, J. Refonaa, Saurav Mallik, D. Dhamodaran, L. K. Joshila Grace, Amel Ksibi, Manel Ayadi, and Tagrid Abdullah N. Alshalali, “An improved adaptive neuro-fuzzy inference framework for lung cancer detection and prediction on Internet of Medical Things platform,” International Journal of Computational Intelligence Systems, vol. 17, pp. 228 (2024).
[14] U. K. Gupta, D. Sethi, and P. K. Goswami, “Adaptive TS-ANFIS neuro-fuzzy controller based single phase shunt active power filter to mitigate sensitive power quality issues in IoT devices,” Advances in Electrical Engineering, Electronics and Energy, vol. 8, pp. 100542 (2024).




