Memristive neuro-fuzzy system

IEEE Transactions on Cybernetics

Volumn 43, Issue 1, 2013, Pages 269-285

  Merrikh Bayat, Farnood ^a   Shouraki, Saeed Bagheri ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Fuzzy inference; Fuzzy relation; Hardware implementation; Memristor crossbar; Neuro fuzzy computing system

Indexed keywords

ARTIFICIAL BRAIN; FUZZY OPERATION; FUZZY RELATIONS; HARDWARE IMPLEMENTATIONS; HIGH CONNECTIVITY; MEMRISTOR; NEURO-FUZZY COMPUTING; NEUROFUZZY SYSTEM;

FAULT TOLERANT COMPUTER SYSTEMS; FUZZY INFERENCE; FUZZY SYSTEMS; HARDWARE; MEMRISTORS; PASSIVE FILTERS;

ANALOG CIRCUITS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPUTER SIMULATION; FUZZY LOGIC; HUMAN; TRANSISTOR;

ALGORITHMS; COMPUTER SIMULATION; FUZZY LOGIC; HUMANS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); TRANSISTORS, ELECTRONIC;

EID: 84890439780     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2012.2205676     Document Type: Article

References (40)

1. T. L. Seng, M. B. Khalid, and R. Yusof, "Tunning of a neuro-fuzzy controller by genetic algorithm," IEEE Trans. Syst.,Man, Cybern. B, Cybern., vol. 29, no. 2, pp. 226-236, 1999.
2. J.-S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference systems," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, May 1993.
3. N. N. Tschichold-Gurman, "The neural network model RuleNet and its application to mobile robot navigation," Fuzzy Sets Syst., vol. 85, no. 2, pp. 287-303, Jan. 1997.
4. H. R. Berenji and P. Khedkar, "Learning and tuning fuzzy logic controllers through reinforcements," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 724-740, Sep. 1992.
5. C. F. Juang and Y. W. Tsao, "A type-2 self-organizing neural fuzzy system and its FPGA implementation," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 6, pp. 1537-1548, Dec. 2008.
6. D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, "The missing memristor found," Nature, vol. 453, no. 7191, pp. 80-83, May 1, 2008.
7. M. Versace and B. Chandler, "The brain of a new machine," IEEE Spectr., vol. 47, no. 12, pp. 30-37, Dec. 2010.
8. L. Rutkowski, Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation. Norwell, MA: Kluwer, 2004.
9. L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning," Inf. Sci., vol. 8, no. 3, pp. 199-249, 1975.
10. J. C. Fodor, "On fuzzy implication operators," Fuzzy Sets Syst., vol. 42, no. 3, pp. 293-300, Aug. 1991.
11. D. Dubois, J. Lang, and H. Prade, "Fuzzy sets in approximate reasoning," Fuzzy Sets Syst., vol. 40, no. 1, pp. 143-202, Mar. 5, 1991.
12. S. B. Shouraki, "A novel fuzzy approach to modeling and control and its hardware implementation based on brain functionality and specifications," Ph.D. dissertation, Univ. Electro-Commun., Chofu, Japan, Mar. 2000.
13. M. Murakami and N. Honda, "A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing," Int. J. Approx. Reason., vol. 45, no. 3, pp. 470-487, Aug. 2007.
14. E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," Int. J. Human-Comput. Stud., vol. 51, no. 2, pp. 135-147, Aug. 1999.
15. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. Syst., Man, Cybern., vol. SMC-15, no. 1, pp. 116-132, Feb. 1985.
16. L. O. Chua, "Memristor-The missing circuit element," IEEE Trans. Circuit Theory, vol. CT-18, no. 5, pp. 507-519, Sep. 1971.
17. Y. V. Pershin, S. L. Fontaine, and M. D. Ventra, "Memristive model of amoeba's learning," Phys. Rev. E, Statist., Nolin. Soft Matter Phys., vol. 80, no. 2, p. 021 926, Aug. 2009.
18. Y. V. Pershin and M. D. Ventra, "Practical approach to programmable analog circuits with memristors," IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 57, no. 8, pp. 1857-1864, Aug. 2010.
19. F. Merrikh-bayat and S. B. Shouraki, "Memristor-based circuits for performing basic arithmetic operations," Procedia Comput. Sci. J., vol. 3, pp. 128-132, 2011.
20. F. Merrikh-Bayat and S. B. Shouraki, "Memristor crossbar-based hardware implementation of IDS method," IEEE Trans. Fuzzy Syst., vol. 19, no. 6, pp. 1083-1096, Dec. 2011.
21. P. Kuekes, "Material implication: Digital logic with memristors," in Memristor Memristive Syst. Symp., UC Berkeley, 2008.
22. B. L. Mouttet, "Proposal for memristors in signal processing," in Proc. Nano-Net Conf., Sep. 2008, vol. 3, pp. 11-13.
23. F. Merrikh-Bayat and S. B. Shouraki, "Mixed analog-digital crossbarbased hardware implementation of sign-sign LMS adaptive filter," Analog Integr. Circuits Signal Process., vol. 66, no. 1, pp. 41-48, Jan. 2011.
24. J. J. Yang, M. D. Pickett, X. Li, D. A. Ohlberg, D. R. Stewart, and R. S. Williams, "Memristive switching mechanism for metal/oxide/metal nanodevices," Nat. Nanotechnol., vol. 3, no. 7, pp. 429-433, 2008.
25. P. J. Kuekes, D. R. Stewart, and R. S.Williams, "The crossbar latch: Logic value storage, restoration, and inversion in crossbar circuits," J. Appl. Phys., vol. 97, no. 3, p. 034 301, Feb. 2005.
26. N. Gergel-Hackett, B. Hamadani, B. Dunlap, J. Suehle, C. Richter, C. Hacker, and D. Gundlach, "A flexible solution-processed memristor," IEEE Electron Device Lett., vol. 30, no. 7, pp. 706-708, Jul. 2009.
27. W. Wang, Q. Yu, C. Xu, and Y. Cui, "Study of filter characteristics based on PWL memristor," in Proc. Int. Conf. Commun., Circuits Syst., 2009, pp. 969-973.
28. S. H. Jo, T. Chang, I. Ebong, B. Bhavitavya, P. Mazumder, and W. Lu, "Nanoscale memristor device as synapse in neuromorphic systems," Nano Lett., vol. 10, no. 4, pp. 1297-1301, Apr. 2010.
29. D. Chabi and J.-O. Klein, "Hight fault tolerance in neural crossbar," in Proc. Int. Conf. Des. Technol. Integr. Syst. Nanoscale Era, 2010, pp. 1-6.
30. K. Cantley, A. Subramaniam, H. Stiegler, R. Chapman, and E. Vogel, "Hebbian learning in spiking neural networks with nano-crystalline silicon TFTs and memristive synapses," IEEE Trans. Nanotechnol., vol. 10, no. 5, pp. 1066-1073, Sep. 2011.
31. A. Afifi, A. Ayatollahi, and F. Raissi, "Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits," in Proc. Eur. Conf. Circuit Theory Des., 2009, pp. 563-566.
32. J. A. Carrasco, C. Zamarreño-Ramos, T. Serrano-Gotarredona, and B. Linares-Barranco, "On neuromorphic spiking architectures for asynchronous STDP memristive systems," in Proc. IEEE Int. Symp. Circuits Syst., 2010, pp. 1659-1662.
33. G. Snider, "Spike-timing-dependent learning in memristive nanodevices," in Proc. IEEE/ACM Int. Symp. Nanoscale Archit., Anaheim, CA, 2008, pp. 85-92.
34. L. V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms and Applications. Upper Saddle River, NJ: Prentice-Hall, 1993.
35. D. O. Hebb, The Organization of Behavior; A Neuropsychological Theory. New York: Wiley-Interscience, 1949.
36. F. Merrikh-Bayat, S. B. Bagheri Shouraki, and I. Esmaili Payeen Afrakoti, "Bottleneck of using single memristor as a synapse and its solution," Neurocomputing. [Online]. Available: http://arxiv.org/abs/1008.3450
37. D. Biolek, Z. Biolek, and V. Biolkova, "SPICE modeling of memristive, memcapacitative and meminductive systems," in Proc. ECCTD, Antalya, Aug. 23-27, 2009, pp. 249-252.
38. M. Butts, A. DeHon, and S. C. Goldstein, "Molecular electronics: Devices, systems and tools for gigagate, gigabit chips," in Proc. Int. Conf. Comput.-Aided Des., 2002, pp. 443-440.
39. J. Huang, M. B. Tahoori, and F. Lombardi, "On the defect tolerance of nano-scale two-dimensional crossbars," in Proc. 19th IEEE Int. Symp. Defect Fault Tolerance VLSI Syst., Oct. 2004, pp. 96-104.
40. C. P. Collier, E. W. Wong, M. Belohradsky, F. M. Raymo, J. F. Stoddart, P. J. Kuekes, R. S.Williams, and J. R. Heath, "Electronically configurable molecular-based logic gates," Science, vol. 285, no. 5426, pp. 391-394, Jul. 1999.