PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2013 | 23 | 2 | 473-483
Tytuł artykułu

Decomposition of the fuzzy inference system for implementation in the FPGA structure

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy systems are discussed. The second allows a decrease in the hardware cost of the fuzzy system and in the computing time of the final result (fuzzy or crisp), especially when referring to relational systems. The physical architecture consists of IP modules implemented in an FPGA structure. The modules can be inserted into or removed from the project to get a desirable fuzzy logic controller configuration. The fuzzy inference system implemented in FPGA can operate with a much higher performance than software implementations on standard microcontrollers.
Rocznik
Tom
23
Numer
2
Strony
473-483
Opis fizyczny
Daty
wydano
2013
otrzymano
2012-05-10
poprawiono
2012-08-17
Twórcy
  • Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-101 Gliwice, Poland
  • Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-101 Gliwice, Poland
Bibliografia
  • Accellera (2002). System Verilog 3.1, www.eda.org/sv/SystemVerilog_3.1a.pdf.
  • Al-Aubidy, K.M. (2010). FPGA-based fuzzy inference system for real-time embedded applications, International Journal of Real-Time Systems 1(1): 9-15.
  • Atmel (2007). 8-bit AVR Microcontroller with 32K Bytes In-System Programmable Flash, 2503-avr-08/07 Edn., ATMEL, www.atmel.com/Images/doc2503.pdf.
  • Baturone, I., Sánchez-Solano, S., Barriga, A. and Huertas, J.L. (1997). Implementation of CMOS fuzzy controllers as mixed-signal integrated circuits, IEEE Transactions on Fuzzy Systems 5(1): 1-19.
  • Bhasker, J. (1998). Verilog HDL Synthesis a Practical Primer, Star Galaxy Publishing, Allentown, PA.
  • Chmiel, M. and Hrynkiewicz, E. (2008). Fast operating bit-byte PLC, 17th World Congress of the International Federation of Automatic Control, Seoul, Korea, pp. 14810-14815.
  • Chojcan, J. and Łęski, J. (2001). Fuzzy Sets and Their Applications, Silesian University of Technology Press, Gliwice, (in Polish).
  • Czogała, E. and Łęski, J. (1998). An equivalence of inference results under defuzzification using both conjunction and logical implication interpretation of fuzzy if-then rules, 6th European Congress on Intelligent Techniques and Soft Computing, 1998, Aachen, Germany, pp. 83-92.
  • Czogała, E. and Pedrycz, W. (1985). Elements and Methods of Fuzzy Set Theory, Polish Scientific Publishers, PWN, Warsaw.
  • Di Nola, A., Pedrycz, W. and Sessa, S. (1984). Decomposition problem of fuzzy relations, International Journal of General Systems 10(2-3): 123-133.
  • Di Nola, A., Pedrycz, W. and Sessa, S. (1985). When is a fuzzy relation decomposable into two fuzzy set, Fuzzy Sets and Systems 16(1): 87-90.
  • Gupta, M.M., Kiszka, J.B. and Trojan, G.M. (1986). Multivariable structure of fuzzy control systems, IEEE Transactions on Systems, Man and Cybernetics 16(5): 638-656.
  • Hollstein, T., Halgamuge, S.K. and Glesner, M. (1996). Computer-aided design of fuzzy systems based on generic VHDL specifications, IEEE Transactions on Fuzzy Systems 4(4): 403-417.
  • Hrynkiewicz, E. and Wyrwoł, B. (2000). Hardware implementation of the FITA fuzzy logic inference systems, Design and Diagnostics of Electronic Circuits and Systems, DDECS, 2000, Smolenice, Slovakia, pp. 169-173.
  • Hung-Ping, C. and Parug, T.-M. (1996). A new approach of multi-stage fuzzy logic inference, Fuzzy Sets and Systems 78(1): 51-72.
  • Hurdon, H.D. (1993). The fuzzy logic expert fan controller, www.ecst.csuchico.edu/˜juliano/Fuzzy/fuzzyfan.
  • Kim, D. (2000). An implementation of fuzzy logic controller on the reconfigurable FPGA system, IEEE Transactions on Industrial Electronics 47(3): 703-715.
  • Kim, D. and Cho, I.-H. (1999). An accurate and cost-effective COG defuzzifier without the multiplier and the divider, Fuzzy Sets and Systems 104(2): 229-244.
  • Kovačić, Z. and Bogdan, S. (2006). Fuzzy Controller Design Theory and Applications, Taylor & Francis Group, LLC, New York, NY.
  • Lee, P.G., Kyun, K.L. and Jeon, G.J. (1995). An index of applicability for the decomposition method of multivariable fuzzy systems, IEEE Transactions on Fuzzy Systems 3(3): 364-369.
  • Martins, A.P. and Carvalho, A.S. (2001). Fuzzy controllers with reduced rulebases and real-time capability for power systems supervision, Electric Power Components and Systems 29(12): 1145-1159.
  • Minns, P. and Elliott, I. (2008). FSM-based Digital Design Using Verilog HDL, John Wiley & Sons, Ltd, New York, NY.
  • Ollero, A. and Garcia-Cerezo, A.J. (1989). Direct digital control, auto-tuning and supervision using fuzzy logic, Fuzzy Sets and Systems 30(2): 135-153.
  • Palnitkar, S. (1996). Verilog HDL: A Guide to Digital Design and Synthesis, SunSoft Press, Upper Saddle River, NJ.
  • Passino, K.M. and Yurkovich, S. (1998). Fuzzy Control, Addison-Wesley Longman, Inc., Menlo Park, CA.
  • Patyra, M.J., Gartner, J.L. and Koster, K. (1996). Digital fuzzy logic controller: Design and implementation, IEEE Transactions on Fuzzy Systems 4(4): 439-459.
  • Piegat, A. (2005). A new definition of the fuzzy set, International Journal of Applied Mathematics and Computer Science 15(1): 125-140.
  • Piegat, A. (2006). What is not clear in fuzzy control systems?, International Journal of Applied Mathematics and Computer Science 16(1): 37-49.
  • Rovatti, R., Guerrieri, R. and Baccarani, G. (1995). An enhanced two-level boolean synthesis methodology for fuzzy rules minimization, IEEE Transactions on Fuzzy Systems 3(3): 288-299.
  • Rutkowska, D., Piliński, M. and Rutkowski, L. (1997). Neural Networks, Genetic Algorithms and Fuzzy Systems, Polish Scientific Publishers, PWN, Warsaw.
  • Sakthivel, G., Anandhi, T.S. and Natarajan, S.P. (2010). Design of optimized fuzzy logic controller for area minimization and its FPGA implementation, International Journal of Computer Science and Network Security 10(8): 187-192.
  • Samsung Electronics (1998). K6T4008C1B Family, 3rd Edn., www.100y.com.tw/pdf_file/K6T4008C1B-GP70.pdf.
  • Siemens AG (1996). Simatic S7 - Fuzzy Control, User Manual.
  • Sulaiman, N., Obaid, Z.A., Marhaban, M.H. and Hamidon, M.N. (2009). FPGA-based fuzzy logic-design and applications: A review, International Journal of Engineering and Technology 1(5): 491-503.
  • Togai InfraLogic, Inc. (1991). FC 110 Digital Fuzzy Processor DFPTM, Irvine, CA.
  • Uppalapati, S. and Kaur, D. (2009). Design and implementation of a Mamdani fuzzy inference system on an FPGA, 28th North American Fuzzy Information Processing Society Annual Conference, NAFIPS2009, Cincinnati, OH, USA, pp. 1-6.
  • Walichiewicz, Ł. (1984). Decomposition of linguistic rules in the design of a multi-dimensional fuzzy control algorithm, Cybernetics and Systems Research, Vienna, Austria, Vol. 2, pp. 557-561.
  • Wyrwoł, B. (2004a). Hardware Implementation of the Fuzzy Inference System Using Programmable Logic Devices, Ph.D. thesis, Silesian University of Technology, Gliwice.
  • Wyrwoł, B. (2004b). Modular fuzzy inference system: Compact defuzzyfication module, 7th Conference on Reprogrammable Digital Circuits, RUC'2004, Szczecin, Poland, pp. 217-224.
  • Wyrwoł, B. (2008). Linguistic decomposition technique based on partitioning the knowledge base of the fuzzy inference system, Bulletin of the Polish Academy of Sciences: Technical Sciences 56(1): 71-76.
  • Wyrwoł, B. (2011). Using graph greedy coloring algorithms in the hardware implementation of the HFIS fuzzy inference system, Electrical Review 87(10): 64-67.
  • Xilinx (2009). Synthesis and Simulation Design Guide, www.xilinx.com/support/documentation/sw_manuals/xilinx11/sim.pdf.
  • Xilinx (2008). DS-001 Spartan II-2,5V FPGA Family, 2.8 Edn. www.xilinx.com/support/documentation/data_sheets/ds001.pdf.
  • Yager, R.R. and Filev, D.P. (1994). Essential of Fuzzy Modelling and Control, John Wiley and Sons, New York, NY.
  • Yamakawa, T. (1989). Stabilization of an inverted pendulum by a high-speed fuzzy logic controller hardware system, Fuzzy Sets and Systems 32(2): 161-180.
  • Zbysiński, P. and Pasierbiński, J. (1992). Programmable Devices-First Steps, BTC Publishing House, Warsaw.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv23z2p473bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.