International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Linguistic Input and Output Variables at n levels for Fuzzy Logic controller using Mamdani approach: Some illustrative examples

Author Affiliations

  • 1Department of Computer Science and Engineering, A.K.S. University, Satna, MP, India
  • 2Comptroller Office, J.N. Agricultural University, Jabalpur, MP, India
  • 3Department of Mathematics and Statistics, J.N. Agricultural University, Jabalpur, MP, India

Res. J. Mathematical & Statistical Sci., Volume 12, Issue (1), Pages 5-15, January,12 (2024)

Abstract

This paper is concerned with Linguistic input and output variables at n levels for fuzzy logic controller using Mamdani approach at various levels of washing time. The definition of triangular membership function at n levels of the variables is given including a set of rules for the controller action. A mixture of triangular and trapezium membership function is defined at five levels. Two different fuzzification methods have been utilized. At the end some illustrative examples have been included.

References

  1. Zadeh, L.A. (1965)., Information and control in Fuzzy Sets., 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65) 90241-X
  2. Dombi, Jozsef and Hussain, Abrar (2020)., A new approach to fuzzy control using the distending function., Journal of Process Control, 86, 16-29.
  3. José Fernando Silva and Sónia F. Pinto (2018)., Power Electronics Handbook (Fourth Edition)., https://doi.org/10.1016/8978-0-12-811407-.00039-6
  4. Ahmadian, M. (2001)., Encyclopedia of Vibration, in Fuzzy Logic Control., https: //doi.org/10.1006/rwvb.2001. 01 93
  5. Das, P. K., Jiao, K., Wang, Y., Barbir, F., & Li, X. (Eds.). (2023)., Fuel Cells for Transportation: Fundamental Principles and Applications., Elsevier.
  6. Pandey, A. K., Singh, V., & Jain, S. (2022)., Study and comparative analysis of perturb and observe (P&O) and fuzzy logic based PV-MPPT algorithms., In Applications of AI and IOT in Renewable Energy, pp. 193-209. Academic Press.
  7. Wang, C. (2015)., A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making., Lehigh University.
  8. Madanda, V. C., Sengani, F., & Mulenga, F. (2023)., Applications of fuzzy theory-based approaches in tunnelling geomechanics: A state-of-the-art review., Mining, Metallurgy & Exploration, 40(3), 819-837.
  9. Mamdani. (1977)., Application of fuzzy logic to approximate reasoning using linguistic synthesis., IEEE transactions on computers, 100(12), 1182-1191.
  10. Mizumoto, M. (2020)., Defuzzification., In Handbook of fuzzy computation. pp. 223-B6. CRC Press.
  11. Samanta, Debasis (2023)., Defuzzification Techniques., CSE, IIT Kharagpur,(U.P.) India.
  12. KASSIM, S. O., Ali, A. G., & Harram, I. M. (2021)., Design aand Implementation of Mamdani Type Fuzzy Inference System Based Water Level Controller., IOSR Journal of Electronics and Communication Engineering, 16(4), 15-22.
  13. Mahajan, V., Agarwal, P., & Gupta, H. O. (2021)., Power quality problems with renewable energy integration., In Power quality in modern power systems. pp. 105-131. Academic Press.
  14. Singh Vivek; Dwivedi Shyam Shankar and Singh Jyoti (2020)., Fuzzy logic control on wash machine., International Research Journal of Engineering and Technology, 07(07), pp 5180-5181.
  15. Princy, S., & Dhenakaran, S. S. (2016)., Comparison of triangular and trapezoidal fuzzy membership function., J. Comput. Sci. Eng, 2(8), 46-51.