Habeeb, N., Abbas, S. (2025). Hardware resources minimization of Bell membership function based on FPGA. , 43(2), 137-148. doi: 10.30684/etj.2024.151873.1786
Najmah A. Habeeb; Saad M. Abbas. "Hardware resources minimization of Bell membership function based on FPGA". , 43, 2, 2025, 137-148. doi: 10.30684/etj.2024.151873.1786
Habeeb, N., Abbas, S. (2025). 'Hardware resources minimization of Bell membership function based on FPGA', , 43(2), pp. 137-148. doi: 10.30684/etj.2024.151873.1786
Habeeb, N., Abbas, S. Hardware resources minimization of Bell membership function based on FPGA. , 2025; 43(2): 137-148. doi: 10.30684/etj.2024.151873.1786

Hardware resources minimization of Bell membership function based on FPGA

Engineering and Technology Journal
Volume 43, Issue 2, February 2025, Pages 137-148    PDF (1003.66 K)
Document Type: Research Paper
DOI: 10.30684/etj.2024.151873.1786
Authors
Najmah A. Habeeb* 1; Saad M. Abbas2
1Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
2Medical Instrumentation Dept., Techniques Engineering, Al-Mustaqbal University, Babil, Iraq.
Abstract
The complexity of hardware realization for the Bell function forces most researchers to replace it with trapezoidal, negatively affecting accuracy. The implementation complexity of the Bell function on FPGA comes from bundling the division operator by FPGA. This requires large hardware resources due to the limited number of logic elements in FPGA devices, such as Lock-Up Tables (LUTs), slices, and DSP48 units, specifically when utilizing the Spartan-3A DSP sc3sd3400a Xilinx FPGA kit. This work investigates hardware resource minimization of Bell Membership Functions (MFs) based on FPGA implementation. A comparative analysis of seven proposed designs with a predesigned system is studied, focusing on resource utilization and performance evaluation. Approach 1 uses the highest number of DSP48 blocks (10). Conversely, Approach 7 achieves the highest area minimization, using the least number of slices (79) and LUTs (144) with zero DSP48 blocks with a reduction rate equal to (97.7%) for slices and (96.7%) LUTs regarding previous work. The absence of multiplication blocks for implementing Approach 7 comes from modifying the precomputation method using half memory size. Performance evaluation indicates varying error rates of 1.06%, 1.14%, and 1.16% for Approaches 1, 2, and 3, respectively. Approaches 6 and 7 exhibit the lowest error rates (0.17%), suggesting superior accuracy, including the previous work. To sum up, the modification in Approach 7 is a very useful method that reduces the hardware platform area and enhances the performance. Thus, this approach is adopted as the best method for designing the Bell function by FPGA.

Highlights
  • The importance of optimizing Bell membership function designs for FPGA implementations was highligted.
  • Seven approaches are proposed to reduce the hardware required for Bell function implementation on FPGA devices.
  • Resource use, performance, and power are analyzed for Bell function designs on a Spartan-3A DSP FPGA kit.
  • The polynomial curve fitting method is segmented to lower equation order and reduce hardware needed for Bell function.
Keywords
ANFIS Bell; shaped fuzzy set Membership Function Hardware resources minimization FPGA
References
  1. N. Talpur, S. J. Abdulkadir, H. Alhussian, M. H. Hasan, N. Aziz, and A. Bamhdi, Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey, Artif. Intell. Rev., 56 (2022) 865-913. https://doi.org/10.1007/S10462-022-10188-3
  2. M. I. S. Guerra, F. M. 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 Syst., 15 (2024) 505-541. https://doi.org/10.1007/S12667-022-00513-8/METRICS
  3. N. Talpur, M. N. M. Salleh, and K. Hussain, An investigation of membership functions on performance of ANFIS for solving classification problems, IOP Conf. Ser. Mater. Sci. Eng., 226 (2017) 012103. https://doi.org/10.1088/1757-899X/226/1/012103
  4. A. Y. Akal and A. E. A. El-Maaty, Fuzzy Assessment of Factors Influencing Quality Level of Highway Projects, Int. J. Manag. Fuzzy Syst., 2 (2016) 6-14. https://doi.org/10.11648/J.IJMFS.20160202.11
  5. A. K. Alazzawi and T. Ercan, Field-Programmable Gate Array Implementation of Adaptive Neuro-Fuzzy System Using Sensors Monitoring Health-Care Medicinal Internet of Things, J. Med. Imaging Heal. Informatics, 10 (2019) 169-177. https://doi.org/10.1166/JMIHI.2020.2693
  6. R. Naderkhani, M. H. Behzad, T. Razzaghnia, and R. Farnoosh, Fuzzy Regression Analysis Based on Fuzzy Neural Networks Using Trapezoidal Data, Int. J. Fuzzy Syst., 23 (2021) 1267-1280. https://doi.org/10.1007/S40815-020-01033-2/METRICS
  7. R. T. Jadhav, A Real Time Linearization of NTC Thermistor using Hybrid Neuro-Fuzzy Logic based on VLSI Technology, Int. J. Emerg. Trends Eng. Res., 8 (2020) 1570-1577. https://doi.org/10.30534/IJETER/2020/16852020
  8. M. Psarakis, A. Dounis, A. Almabrok, S. Stavrinidis, and G. Gkekas, An FPGA-Based Accelerated Optimization Algorithm for Real-Time Applications, J. Signal Process. Syst., 92 (2020)1155-1176. https://doi.org/10.1007/S11265-020-01522-5/METRICS
  9. E. Noorsal, S. Arof, S. Z. Yahaya, Z. Hussain, D. Kho, and Y. M. Ali, Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model, Micromachines, 12 (2021) 968. https://doi.org/10.3390/MI12080968
  10. M. Hermassi, S. Krim, Y. Kraiem, and M. A. Hajjaji, Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation, Comput. Electr. Eng., 117 (2042) 109264. https://doi.org/10.1016/J.COMPELECENG.2024.109264
  11. Ó. Mata-Carballeira, J. Gutiérrez-Zaballa, I. Del Campo, and V. Martínez, An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance, Sensors, 19 (2019) 4011. https://doi.org/10.3390/S19184011
  12. P. B. Indira and R. D. Krishna, Optimized adaptive neuro fuzzy inference system (OANFIS) based EEG signal analysis for seizure recognition on FPGA, Biomed. Signal Process. Control, 66 (2021) 102484. https://doi.org/10.1016/J.BSPC.2021.102484
  13. S. H. Klidbary, S. B. Shouraki, and B. Linares-Barranco, Digital hardware realization of a novel adaptive ink drop spread operator and its application in modeling and classification and on-chip training, Int. J. Mach. Learn. Cybern., 10 (2019) 2541-2561. https://doi.org/10.1007/S13042-018-0890-X/METRICS
  14. F. Karataş, İ. Koyuncu, M. Tuna, M. Alçın, Bulanık Mantık Üyelik Fonksiyonlarının FPGA Üzerinde Gerçeklenmesi, Bilgisayar Bilimleri ve Teknolojileri Dergisi, 1 (2020) 1-9. http://acikerisim.klu.edu.tr/xmlui/handle/20.500.11857/1336
  15. R. yousif, I. Hashim, and B. Abd, Implementation of Hyperbolic Sine and Cosine Functions Based on FPGA using different Approaches, Eng. Technol. J., 41 (2023) 1-16. https://doi.org/10.30684/ETJ.2023.139756.1440
  16. R. K. Yousif, I. A. Hashim, and B. H. Abd, Low Area FPGA Implementation of Hyperbolic Tangent Function, 6th Iraqi Int. Conf. Eng. Technol., its Appl. IICETA 2023, pp. 596-602, 2023. https://doi.org/10.1109/IICETA57613.2023.10351345
  17. H.H. Thannoon, I.A .Hashim‏, FPGA Implementation of Efficient Adaptive Filter Incorporating Systolic Architecture, Eng. Technol. J., 2 (2024) 261 -275. http://doi.org/10.30684/etj.2023.142877.1548
  18. S. Gupta, P. K. Biswas, B. Aljafari, S. B. Thanikanti, and S. K. Das, Modelling, simulation and performance comparison of different membership functions based fuzzy logic control for an active magnetic bearing system, J. Eng., 2023 (2023) e12229. https://doi.org/10.1049/tje2.12229
  19. M. Yilmaz, Evaluation of Total Antioxidant Capacity (TAS) by Using Fuzzy Logic, J. Adv. Math. Comput. Sci., 8 (2015) 433-446. https://doi.org/10.9734/BJMCS/2015/15919
  20. W. Guang, M. Baraldo, and M. Furlanut, Calculating percentage prediction error: a user’s note, Pharmacol. Res., 32 (1995) 241-248. https://doi.org/10.1016/S1043-6618(05)80029-5
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