An adaptive efficient memristive ink drop spread (IDS) computing system

  • Sajad Haghzad KlidbaryEmail author
  • Saeed Bagheri Shouraki
  • Iman Esmaili Paeen Afrakoti
Original Article


Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of ALM in different applications, an efficient hardware implementation has remained a challenge, which is mainly due to considerably high memory requirement of IDS operation. In this paper, in a novel approach to IDS operation, we propose an abstract representation of the IDS planes which minimizes the memory requirement and the computational cost, and consequently, benefits the hardware implementation in terms of area and speed. The proposed approach is fully compatible with memristor-crossbar implementation with an adaptive learning capability. Simpler learning algorithm and higher speed make our proposed algorithm suitable for applications where real-time process, low-cost and small implementation are of high priority. Applications in the classification of real-world datasets and function approximation are provided to confirm the effectiveness of the algorithm. Eventually, the paper concludes that the proposed computing structure provides a synergy between artificial neural networks and fuzzy domains.


Active learning method (ALM) Memristor-crossbar/CMOS Function approximation Classification 



The authors would like to thank Soroush Sheikhpour Kourabbaslou and Mohammad Bavandpour for their kind discussions. The first author is grateful to Iran National Science Foundation (INSF), which has partially supported the present research (Grant No. 96000943).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84CrossRefGoogle Scholar
  2. 2.
    Shouraki SB (2000) A novel fuzzy approach to modeling and control and its hardware implementation based on brain functionality and specifications. The University of Electro-Communication, Chofu-TokyoGoogle Scholar
  3. 3.
    Shouraki SB, Honda N (1999) Simulation of brain learning process through a novel fuzzy hardware approach. In: 1999 IEEE international conference and proceedings on IEEE SMC’99 systems, man, and cybernetics, 1999. IEEEGoogle Scholar
  4. 4.
    Shouraki SB, Honda N, Yuasa G (1999) Fuzzy interpretation of human intelligence. Int J Uncertain Fuzziness Knowl Based Syst 7(04):407–414CrossRefzbMATHGoogle Scholar
  5. 5.
    Sugeno M, Yasukawa T (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1(1):7–31CrossRefGoogle Scholar
  6. 6.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. Syst Man Cybern IEEE Trans 1:116–132CrossRefzbMATHGoogle Scholar
  7. 7.
    Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. Syst Man Cybern IEEE Trans 23(3):665–685CrossRefGoogle Scholar
  8. 8.
    Murakami M, Honda N (2007) A study on the modeling ability of the IDS method: a soft computing technique using pattern-based information processing. Int J Approx Reason 45(3):470–487CrossRefzbMATHGoogle Scholar
  9. 9.
    Shouraki SB, Honda N (1998) Fuzzy controller design by an active learning method. In: 31th symposium of intelligent controlGoogle Scholar
  10. 10.
    Shahdi SA, Shouraki SB (2002) Supervised active learning method as an intelligent linguistic controller and its hardware implementation. In: 2nd IASTEAD international conference on artificial intelligence and applications (AIA’02), Malaga, SpainGoogle Scholar
  11. 11.
    Sakurai Y, Honda N, Nishino J (2003) Acquisition of control knowledge of nonholonomic system by active learning method. In: IEEE international conference on systems, man and cybernetics, 2003. IEEEGoogle Scholar
  12. 12.
    Sagha H, Afrakoti IEP, Bagherishouraki S (2013) Actor-critic-based ink drop spread as an intelligent controller. Turk J Electr Eng Comput Sci 21(4):1015–1034Google Scholar
  13. 13.
    Sagha H, et al (2008) Real-time IDS using reinforcement learning. In: Second international symposium on intelligent information technology application, 2008. IITA’08. IEEEGoogle Scholar
  14. 14.
    Klidbary SH, et al (2017) Outlier robust fuzzy active learning method (ALM). In: 7th international conference on computer and knowledge engineering (ICCKE), 2017. IEEEGoogle Scholar
  15. 15.
    Firouzi M, Shouraki SB, Afrakoti IEP (2014) Pattern analysis by active learning method classifier. J Intell Fuzzy Syst 26(1):49–62MathSciNetzbMATHGoogle Scholar
  16. 16.
    Shahraiyni TH et al (2007) Application of the Active Learning Method for the estimation of geophysical variables in the Caspian Sea from satellite ocean colour observations. Int J Remote Sens 28(20):4677–4683CrossRefGoogle Scholar
  17. 17.
    Murakami M, Honda N, Nishino J (2004) A high performance IDS processing unit for a new fuzzy-based modeling. In: IEEE international conference and proceedings on fuzzy systems, 2004. IEEEGoogle Scholar
  18. 18.
    Firouzi M, et al (2010) A novel pipeline architecture of replacing ink drop spread. In: 2010 second world congress on nature and biologically inspired computing (NaBIC). IEEEGoogle Scholar
  19. 19.
    Tarkhan M, Shouraki SB, Khasteh SH (2009) A novel hardware implementation of IDS method. IEICE Electron Express 6(23):1626–1630CrossRefGoogle Scholar
  20. 20.
    Merrikh-Bayat F, Shouraki SB, Rohani A (2011) Memristor crossbar-based hardware implementation of the IDS method. Fuzzy Syst IEEE Trans 19(6):1083–1096CrossRefGoogle Scholar
  21. 21.
    Afrakoti IEP, Shouraki SB, Haghighat B (2014) An optimal hardware implementation for active learning method based on memristor crossbar structures. Syst J IEEE 8(4):1190–1199CrossRefGoogle Scholar
  22. 22.
    Chua LO (1971) Memristor-the missing circuit element. Circuit Theory IEEE Trans 18(5):507–519CrossRefGoogle Scholar
  23. 23.
    Chua LO, Kang SM (1976) Memristive devices and systems. Proc IEEE 64(2):209–223MathSciNetCrossRefGoogle Scholar
  24. 24.
    Strukov DB et al (2008) The missing memristor found. Nature 453(7191):80–83CrossRefGoogle Scholar
  25. 25.
    Eshraghian K et al (2011) Memristor MOS content addressable memory (MCAM): hybrid architecture for future high performance search engines. IEEE Trans Very Large Scale Integr VLSI Syst 19(8):1407–1417CrossRefGoogle Scholar
  26. 26.
    Snider G et al (2011) From synapses to circuitry: using memristive memory to explore the electronic brain. Computer 2:21–28CrossRefGoogle Scholar
  27. 27.
    Klidbary SH, Shouraki SB, Afrakoti IEP (2016) Fast IDS computing system method and its memristor crossbar-based hardware implementation. arXiv:1602.06787
  28. 28.
    Waser R, Aono M (2007) Nanoionics-based resistive switching memories. Nat Mater 6(11):833–840CrossRefGoogle Scholar
  29. 29.
    Bavandpour M et al (2014) Spiking neuro-fuzzy clustering system and its memristor crossbar based implementation. Microelectron J 45(11):1450–1462CrossRefGoogle Scholar
  30. 30.
    Prezioso M et al (2015) Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521(7550):61–64CrossRefGoogle Scholar
  31. 31.
    Saïghi S et al (2015) Plasticity in memristive devices for spiking neural networks. Front Neurosci 9:51CrossRefGoogle Scholar
  32. 32.
    Prezioso M et al (2016) Self-adaptive spike-time-dependent plasticity of metal-oxide memristors. Sci Rep 6:21331CrossRefGoogle Scholar
  33. 33.
    Li T et al (2016) A spintronic memristor-based neural network with radial basis function for robotic manipulator control implementation. IEEE Trans Syst Man Cybern Syst 46(4):582–588CrossRefGoogle Scholar
  34. 34.
    Li T, et al (2016) An improved design of RBF neural network control algorithm based on spintronic memristor crossbar array. Neural Comput Appl 1–8Google Scholar
  35. 35.
    Perez-Carrasco J, et al (2010) On neuromorphic spiking architectures for asynchronous STDP memristive systems. In: Proceedings of 2010 IEEE international symposium on circuits and systems (ISCAS). IEEEGoogle Scholar
  36. 36.
    Pershin YV, La Fontaine S, Di Ventra M (2009) Memristive model of amoeba learning. Phys Rev E 80(2):021926CrossRefGoogle Scholar
  37. 37.
    Bayat FM, Shouraki SB (2015) Nonlinear behavior of memristive devices during tuning process and its impact on STDP learning rule in memristive neural networks. Neural Comput Appl 26(1):67–75CrossRefGoogle Scholar
  38. 38.
    Kuekes P (2008) Material implication: digital logic with memristors. In: A presentation in the memristor and memristive systems symposium at UC BerkeleyGoogle Scholar
  39. 39.
    Raja T, Mourad S (2009) Digital logic implementation in memristor-based crossbars. In: IEEE international conference on communications, circuits and systems, 2009 (ICCCAS)Google Scholar
  40. 40.
    Shin S, Kim K, Kang S-M (2009) Memristor-based fine resolution programmable resistance and its applications. In: International conference on communications, circuits and systems, 2009 (ICCCAS 2009), IEEEGoogle Scholar
  41. 41.
    Pershin YV, Ventra MD (2010) Practical approach to programmable analog circuits with memristors. Circuits Syst I Regul Pap IEEE Trans 57(8):1857–1864MathSciNetCrossRefGoogle Scholar
  42. 42.
    Merrikh-Bayat F, Shouraki SB (2010) Memristor-based circuits for performing basic arithmetic operations. arXiv:1008.3452
  43. 43.
    Merrikh-Bayat F, Bagheri-Shouraki S (2011) Mixed analog-digital crossbar-based hardware implementation of sign–sign LMS adaptive filter. Analog Integr Circ Sig Process 66(1):41–48CrossRefGoogle Scholar
  44. 44.
    Cho K, Lee S-J, Eshraghian K (2015) Memristor-CMOS logic and digital computational components. Microelectron J 46(3):214–220CrossRefGoogle Scholar
  45. 45.
    Truong SN et al (2015) New twin crossbar architecture of binary memristors for low-power image recognition with discrete cosine transform. IEEE Trans Nanotechnol 14(6):1104–1111CrossRefGoogle Scholar
  46. 46.
    Hasan R, Taha TM, Yakopcic C (2017) On-chip training of memristor crossbar based multi-layer neural networks. Microelectron J 66:31–40CrossRefGoogle Scholar
  47. 47.
    Klidbary SH, Shouraki SB (2018) A novel adaptive learning algorithm for low-dimensional feature space using memristor-crossbar implementation and on-chip training. Appl Intell. Google Scholar
  48. 48.
    Mouttet B (2009) Proposal for memristors in signal processing. nano-net. Springer, Berlin, pp 11–13CrossRefGoogle Scholar
  49. 49.
    Sheridan PM et al (2017) Sparse coding with memristor networks. Nature Nanotechnol 12:784–789CrossRefGoogle Scholar
  50. 50.
    Kolka Z, Biolek D, Biolkova V (2015) Improved model of TiO2 memristor. Radioengineering 24(2):378–383CrossRefGoogle Scholar
  51. 51.
    Biolek D et al (2015) Reliable modeling of ideal generic memristors via state-space transformation. Radioengineering 24(2):393–407CrossRefGoogle Scholar
  52. 52.
    Naous R, Al-Shedivat M, Salama KN (2016) Stochasticity modeling in memristors. IEEE Trans Nanotechnol 15(1):15–28CrossRefGoogle Scholar
  53. 53.
    Juang C-F, Tsao Y-W (2008) A type-2 self-organizing neural fuzzy system and its FPGA implementation. Syst Man Cybern Part B Cybern IEEE Trans 38(6):1537–1548CrossRefGoogle Scholar
  54. 54.
    Yi Y et al (2016) FPGA based spike-time dependent encoder and reservoir design in neuromorphic computing processors. Microprocess Microsyst 46:175–183CrossRefGoogle Scholar
  55. 55.
    Afrakoti IEP et al (2017) Using a memristor crossbar structure to implement a novel adaptive real-time fuzzy modeling algorithm. Fuzzy Sets Syst 307:115–128MathSciNetCrossRefGoogle Scholar
  56. 56.
    Lang KJ (1988) Learning to tell two spirals apart. In: Proceedings of 1988 connectionist models summer school, Pittsburgh, PAGoogle Scholar
  57. 57.
    Sagha H, et al (2008) Genetic ink drop spread. In: Second international symposium on intelligent information technology application, IITA’08. IEEEGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Artificial Creatures Laboratory (ACL), Research Group for Brain Simulation and Cognitive Science, Department of Electrical EngineeringSharif University of TechnologyTehranIran
  2. 2.Faculty of Engineering and TechnologyUniversity of MazandaranBabolsarIran

Personalised recommendations