Fuzzy processors are used for control actions in nonlinear mechatronic systems where high processing speed is required. The Field Programmable Gate Arrays (FPGA) are a good option to implement low cost fuzzy hardware in a short development time. A very important block in fuzzy hardware is the fuzzifier, since it affects directly in the accuracy of the result and in the processing time for obtaining a fuzzy number. There have been many design methodologies intended for enhancing the performance of this block. This paper presents a parallel fuzzifier circuit called α-BSSF. Its main design characteristics are the use of α-levels for membership representation, usage of integer numbers, and avoiding time-consuming operations. As result, we obtained a fuzzifier that shows advantages in the reduction of the response time and computational resources against the existing sequential fuzzification methods. This proposal is targeted not only for T1FS, but also for T2FS, since the membership calculation through fuzzifier is applied in the same way but twice.
del CampoI., EchanobeJ., BasterretxeaK. and BosqueG., Scalable Architecture for High-Speed Multidimensional Fuzzy Inference Systems, Journal of Circuits Systems and Computers20(03) (2011), 375–400.
2.
TroncosoR. de J.R., Electronica digital y logica programable, 2nd ed. Mexico: Universidad de Guananjuato, (2008).
3.
BallJ., Designing Soft-Core Processors for FPGAs, Springer (2007), 229–256.
4.
MurshidA.M., LoanS.A., AbbasiS.A. and AlamoudA.R.M., VLSI architecture of fuzzy logic hardware implementation: A review, Int J Fuzzy Syst13(2) (2011), 74–88.
5.
HernandezA., Fuzzy Hardware: A Retrospective and Analysis, IEEE Trans Fuzzy Syst20(4) (2012), 623–635.
6.
BosqueG., del CampoI. and EchanobeJ., Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades, Eng Appl Artif Intell32(1) (2014), 283–331.
7.
CardarilliG.C., ReM. and LojacanoR., VLSI implementation of a real time fuzzy processor, Journal of Intelligent and Fuzzy Systems (1998), 389–401.
8.
PrzybylA. and JooM., The method of Hardware Implementation of Fuzzy Systems on FPGA, in, Intenational Conference of Artificial Intelligence and soft computing (ICAISC)9692 (2016), 284–298.
9.
BehroozP., Computer Architecture: From Microprocessor To Supercomputers. Mexico: McGraw-Hill, (2007).
10.
MendelJ.M., Type-2 Fuzzy Sets and Systems: An Overview, IEEE Comput Intell Mag (2007), 20–29.
11.
AnandM.S., Design and Implementation of Fuzzy Controller on FPGA, I.J. Intell. Syst. Appl10 (2012), 35–42.
12.
RamadanE.A.E.-H.M., El-BardiniM., El-RabaieN.M. and FkirinM.A., Embedded system based on a real time fuzzy motor speed controller, Ain Shams Eng J5(2) (2014), 399–409.
13.
NedjahN., RenatoP., SandresS.S. and De MacedoL., Expert Systems with Applications Customizable hardware design of fuzzy controllers applied to autonomous car driving, Expert Syst Appl41(16) (2014), 7046–7060.
14.
ChowdhuryS. R., High - resolution detection of sustained ventricular and supraventricular tachycardia through FPGA - based fuzzy processing of ECG signal, Med Biol Eng Comput53(10) (2015), 1037–1047.
15.
AboodA.G., A New Hardware Architecture for Fuzzy Logic System Acceleration, Iraq J Electr Electron Eng12(2) (2016), 188–198.
16.
MasmoudiM.M., KrichenM.S., KoesdwiadyN. and KarrayA.B., F. Design and FPGA Implementation of a Fuzzy-PI Controller for Omnidirectional Robot System, Intell Technol Appl 4447 (2017), 141–152.
17.
YoussefA., ElM. and ZekryA., Recon fi gurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems, Renew Sustain Energy Rev82(2017) (2018), 1313–1319.
18.
Haji Seyed JavadiM., MahdianiH.R. and Zeinali KhE., A hardware oriented fuzzification algorithm and its VLSI implementation, Soft Comput17(4) (2013), 683–690.
19.
ShareefH., HusseinA. and MohamedA., Neurocomputing A novel approach for fuzzy logic PV inverter controller optimization using lightning search algorithm, Neurocomputing168 (2015), 435–453.
20.
VuongP.T., MadniA.M. and VuongJ.B., VHDL implementation for a fuzzy logic controller, IEEE Conference publicarions, 6∘ World Automation Congress (WAC). Budapest, Hungry, (2007).
21.
RamadanE.A.E.H.M., El-BardiniM., El-RabaieN.M. and FkirinM.A., Embedded system based on a real time fuzzy motor speed controller, Ain Shams Eng J5(2) (2014), 399–409.
22.
BedoudK., Ali-RachediM., BahiT. and LakelR., Adaptive Fuzzy Gain Scheduling of PI Controller for control of the Wind Energy Conversion Systems, Energy Procedia74 (2015), 211–225.
23.
PandaA.K., RameshT. and KumarS.S., Rotor- fl ux-based MRAS speed estimator for DTFC-SVM of a speed sensorless induction motor drive using Type-1 and Type-2 fuzzy logic controllers over a wide speed range, (2016), 1863–1881.
24.
HagrasH. A., “A Hierarchical Type-2 Fuzzy Logic Control Architecture for Autonomous Mobile Robots,”, 12(4) (2004), 524–539.
25.
ZadehL.A., The concept of a linguistic truth variable and its application to approximate reasoning-I, II, III, Inform Sci8199–249, 301–357, 43–80, 1975., 1975.
26.
JaramilloJ.A. and GuzmanI., VHDL Guia de Estilo y Practicas de Laboratorio de circuitos Logicos. Instituto Politecnico Nacional, (2011).
27.
MahmoudiehA., Digital Hardware Implementing of Fuzzy based Roll Control System of Digital Hardware Implementing of Fuzzy based Roll Control System of Unmanned Aerial Vehicle, in The 11th Iranian Conference on Fuzzy Systems (2018), 261–266.
28.
BoukakaS., TeiarH., ChaouiH. and SicardP., FPGA implementation of an adaptive fuzzy logic controller for PMSM, 7th IET Int Conf Power Electron Mach Drives (2014), 1–6.
29.
ChenC.H., WangC.C., WangY.T. and WangP.T., Fuzzy Logic Controller Design for Intelligent Robots, Math Probl Eng (2017).
30.
FuY., LiH. and KayeM.E., Hardware/software codesign for a fuzzy autonomous road-following system, IEEE Trans Syst Man Cybern Part C Appl Rev40(6) (2010), 690–696.
31.
ChangK.C., Digital Systmes Desig with VHDL ans Synthesis: An Integrated Approach. Willey India Edition, (2012).
32.
SurmanH., Optimized Fuzzy Controller Arquitecture for Field Programable Gate Array, (1993).
33.
DavidJ.L.H. and PattersonA., Computer Organization and Desig, 3th ed. Unites States of America: Morgan Kaufmann, (2005).
34.
S.A. and, MurshidA.M., A Novel VLSI architecture of a Multi Membership Function Based MAX-MIN Calculator Circuit, in International Conference On Advanced Electronic Systems (ICAES), (2013).
35.
JouJ.M., ChenP. and YangS., An Adaptive Fuzzy Logic Controller: Its VLSI Architecture and Applications, 8(1) (2000), 52–60.
36.
MurshidA.M. and LoanS.A., Architectural Design of Fuzzy Inference Processor using Triangular-Shaped Membership Function, 25(2) (2011), 16–20.
37.
LoanS.A. and MurshidA., A Novel Fuzzy Inference Processor usingTrapezoidal-Shaped Membership Function, no. September, (2011).
38.
LoanA.M., MushidS.A., AbassiA.M. and AlamoudS.A., A novel VLSI architecture for a fuzzy inference processor using Gaussian shaped membership function, Jounal Intell Fuzzy Syst24(1) (2013), 15–19.
39.
ShiramR.S. and AnirbanH.S., Jyoti Hati and Rajiv, A Novel Processor Architecture for Sugeno-type Fuzzy Inference Systems, In Conf Adv Electron Syst (2013), 117–121.
40.
NowakováJ., PokornýM. and PiešM., Conventional controller design based on Takagi-Sugeno fuzzy models, J Appl Log13(2) (2015), 148–155.
41.
SunY., TangS., MengZ., ZhaoY. and YangY., A scalable accuracy fuzzy logic controller on FPGA, Expert Syst Appl42(19) (2015), 6658–6673.
