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Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

  • Tomasz TalaśkaEmail author
Open Access
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Abstract

Fuzzy systems play an important role in many industrial applications. Depending on the application, they can be implemented using different techniques and technologies. Software implementations are the most popular, which results from the ease of such implementations. This approach facilitates modifications and testing. On the other hand, such realizations are usually not convenient when high data rate, low cost per unit, and large miniaturization are required. For this reason, we propose efficient, fully digital, parallel, and asynchronous (clock-less) fuzzy logic (FL) systems suitable for the implementation as ultra low-power-specific integrated circuits (ASICs). On the basis of our former work, in which single FL operators were proposed, here we demonstrate how to build larger structures, composed of many operators of this type. As an example, we consider Lukasiewicz neural networks (LNN) that are fully composed of selected FL operators. In this work, we propose FL OR, and AND Lukasiewicz neurons, which are based on bounded sum and bounded product FL operators. In the comparison with former analog implementations of such LNNs, digital realization, presented in this work, offers important advantages. The neurons have been designed in the CMOS 130nm technology and thoroughly verified by means of the corner analysis in the HSpice environment. The only observed influence of particular combinations on the process, voltage, and temperature parameters was on delays and power dissipation, while from the logical point of view, the system always worked properly. This shows that even larger FL systems may be implemented in this way.

Keywords

Fuzzy logic systems FL operators FL neural networks Asynchronous circuits Parallel circuits CMOS implementation 

Mathematics Subject Classification (2010)

68T27 

Notes

References

  1. 1.
    Tan, Q., Wei, Q., Hu, J., Aldred, D.: Road vehicle detection using fuzzy logic rule-based method. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 3 (2010)Google Scholar
  2. 2.
    Sharma, K., Kumar Palwalia, P.: A modified PID control with adaptive fuzzy controller applied to DC motor. In: International Conference on Information, Communication, Instrumentation and Control (ICICIC) (2017)Google Scholar
  3. 3.
    Chen, Z., Gomez, S.A., McCormick, M.: A fuzzy logic controlled power electronic system for variable speed wind energy conversion systems. In: International Conference on Power Electronics and Variable Speed Drives (2000)Google Scholar
  4. 4.
    Sreedivya, K.M., Aruna Jeyanthy, P., Devaraj, D.: Fuzzy logic based power system stabilizer for damping low frequency oscillations in power system. In: International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT) (2017)Google Scholar
  5. 5.
    Seker, H., Odetayo, M.O., Petrovic, D., Naguib, R.N.G.: A fuzzy logic based-method for prognostic decision making in breast and prostate cancers. IEEE Trans. Inf. Technol. Biomed. 7, 2 (2003)CrossRefGoogle Scholar
  6. 6.
    Cetin, O., Kurnaz, S., Kaynak, O.: Fuzzy logic based approach to design of autonomous landing system for unmanned aerial vehicles. J. Intell. Robot. Syst. 61, 239–250 (2011)CrossRefGoogle Scholar
  7. 7.
    Jin, M., Zhao J., Jin J., Yu G., Li W.: The adaptive Kalman filter based on fuzzy logic for inertial motion capture system. Measurement, Elsevier 7, 196–204 (2014)CrossRefGoogle Scholar
  8. 8.
    Banach, M., Wasilewska, A., Długosz, R., Pauk, J.: Novel techniques for a wireless motion capture system for the monitoring and rehabilitation of disabled persons for application in smart buildings. Technol. Health Care, IOS Press 26(S2), 671–677 (2018)CrossRefGoogle Scholar
  9. 9.
    Milanés, V., Villagrá, J., Godoy, J., Simó, J., Pérez, J., Onieva, E.: An intelligent V2I-Based traffic management system. IEEE Trans. Intell. Transp. Syst. 1(49-58), 13 (2012)Google Scholar
  10. 10.
    Salman, M.A., Ozdemir S., Celebi F.V.: Fuzzy traffic control with vehicle-to-everything communication, Sensors,  https://doi.org/10.3390/s1802036827, (368) (2018)
  11. 11.
    Banach, M., Długosz, R.: Real-time locating systems for smart city and intelligent transportation applications. In: IEEE 30th International Conference on Microelectronics (Miel 2017) (231-234) (2017)Google Scholar
  12. 12.
    Li, T.H.S., Chen, Ch.-Y., lim, K.-CH.: Combination of fuzzy logic control and back propagation neural networks for the autonomous driving control of car-like mobile robot systems. In: Proceedings of SICE Annual Conference (2010)Google Scholar
  13. 13.
    Kayacan, E., Kayacan, E.L., Ramon, H., Saeys, W.: Adaptive Neuro-Fuzzy control of a spherical rolling robot using Sliding-Mode-Control-Theory-Based online learning algorithm. IEEE Transactions on Cybernetics 43, 1 (2013)CrossRefGoogle Scholar
  14. 14.
    Allah Hooshmand, R., Parastegari, M., Forghani, Z.: Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers. IEEE Electr. Insul. Mag. 28, 5 (2012)CrossRefGoogle Scholar
  15. 15.
    Yen, J., Langari, R., Zadeh, L.A.: Industrial Applications of Fuzzy Logic and Intelligent Systems. IEEE Press, New York (1995)zbMATHGoogle Scholar
  16. 16.
    Nagaraj, R., Mayurappriyan, P.S., Jerome, J.: Microcontroller based fuzzy logic technique for dc-dc converter. In: International Conference on Power Electronics (2006)Google Scholar
  17. 17.
    Rudas, I.J., Batyrshin, I.Z., Hernández Zavala, A., Camacho Nieto, O., Horváth, L., Villa Vargas, L.: Generators of fuzzy operations for hardware implementation of fuzzy systems, advances in artificial intelligence. In: 7th Mexican International Conference on Artificial Intelligence (MICAI) (2008)Google Scholar
  18. 18.
    Meisam Ramzanzad. M., Rashidy Kanan, H.: A new method for design and implementation of intelligent traffic control system based on fuzzy logic using FPGA. In: Iranian Conference on Fuzzy Systems (IFSC) (2013)Google Scholar
  19. 19.
    Guo, S., Peters, L., Surmann, H.: Design and application of an analog fuzzy logic controller. IEEE Trans. Fuzzy Syst. 4, 4 (1996)CrossRefGoogle Scholar
  20. 20.
    Yamakawa, T., Miki, T.: The current mode fuzzy logic integrated circuits fabricated by the standard CMOS process. IEEE Trans. Comput. C-35, 2 (1986)CrossRefGoogle Scholar
  21. 21.
    Długosz, R., Pedrycz, W.: Łukasiewicz fuzzy logic networks and their ultra low power hardware implementation, Neurocomputing, Elsevier, 73 (2010)Google Scholar
  22. 22.
    Sanchez-Solano, S., Barriga, A., Jimenez, C.J., Huertas, J.L.: Design and application of digital fuzzy controllers. In: International Fuzzy Systems Conference (1997)Google Scholar
  23. 23.
    Baturone, I., Sanchez-Solano, S., Barriga, A., Huertas, J.: Implementation of CMOS fuzzy controllers as Mixed-Signal integrated circuits. IEEE Trans. Fuzzy Syst. 5, 1 (1997)CrossRefGoogle Scholar
  24. 24.
    Talaśka, T., Długosz, R., Skruch, P.: Efficient transistor level implementation of selected fuzzy logic operators used in control systems. In: Advances in Intelligent Systems and Computing, Trends in Advanced Intelligent Control, Optimization and Automation, vol. 577. Springer (2017)Google Scholar
  25. 25.
    Talaśka, T.: Implementation of fuzzy logic operators as digital asynchronous circuits in CMOS technology. In: International Conference on Microelectronics (MIEL) (2017)Google Scholar
  26. 26.
    Navi, K., Doostaregan, A., Moaiyeri, M., Hashemipour, O.: A hardware-friendly arithmetic method and efficient implementations for designing digital fuzzy adders. Fuzzy Set. Syst, Elsevier 185(1), 111–124 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Zavala, A.H., Batyrshin, L.Z., Nieto, O.C., Castillo, O.: Conjunction and disjunction operations for digital fuzzy hardware. Appl. Soft. Comput. 13, 7 (2013)CrossRefGoogle Scholar

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© The Author(s) 2019

OpenAccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Faculty of Telecommunication, Computer Science and Electrical EngineeringUTP University of Science and TechnologyBydgoszczPoland

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