Skip to main content
Springer Nature Link
Log in

Exploring the New Application of Morphological Neural Networks

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

Included in the following conference series:

  • 3120 Accesses

Abstract

The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. In this paper we took advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time. Theoretical analysis and simulation experiments show that the method of morphological associative memory networks is a higher effective machine learning method, and can very well simulate the cognitive phenomenon of “one-trial learning”, therefore, it will provide a new experimental tool for the study of intelligent science and cognitive science.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Brosgole, L., Contino, A.F., Hansen, K.H.: What is one-trial learning? Psychon. Sci. 15(2), 89–90 (1969)

    Article  Google Scholar 

  2. Sariyska, R., Lachmann, B., Markett, S.: Individual differences in implicit learning abilities and impulsive behavior in the context of Internet addiction and Internet Gaming Disorder under the consideration of gender. Addict. Behav. Rep. 5, 19–28 (2017)

    Article  Google Scholar 

  3. Ziori, E., Dienes, Z.: The time course of implicit and explicit concept learning. Conscious. Cogn. 21(1), 204–216 (2012)

    Article  Google Scholar 

  4. Zhan, Y., Luo, Y.Z., Deng, X.F.: Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm. Atmos. Environ. 155, 129–139 (2017)

    Article  Google Scholar 

  5. Barber, T.A.: Discrimination of shape and size sues by day-old chicks in two one-trial learning tasks. Behav. Process. 124, 10–14 (2016)

    Article  Google Scholar 

  6. Feng, N.Q., Qin, L.J., Wang, X.F.: Morphological associative memories applied to the implicit learning. J. Henan Normal Univ. (Nat. Sci. Ed.) 41(3), 156–159 (2013)

    Google Scholar 

  7. Yu, J.Y.: Cognitive psychology and neural networks. In: Cao, C.G., Zhou, Z.H. (eds.) Neural Networks and their Applications, pp. 406–445. Tsinghua University Press, Beijing (2004)

    Google Scholar 

  8. Taffese, W.Z., Sistonen, E.: Machine learning for durability and service-life assessment of reinforced concrete structures: recent advances and future directions. Autom. Constr. 77, 1–14 (2017)

    Article  Google Scholar 

  9. Davidson, J.L., Hummer, F.: Morphology neural networks: an introduction with applications. Circuits Syst. Signal Process. 12(2), 177–210 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  10. Ritter, G.X., Sussner, P., Dia-de-Leon, J.L.: Morphological associative memories. IEEE Trans. Neural Netw. 9(2), 281–292 (1998)

    Article  Google Scholar 

  11. Wang, M., Wang, S.T., Wu, X.J.: Initial results on fuzzy morphological associative memories. Acta Electronica Sinica 31(5), 690–693 (2003)

    MathSciNet  Google Scholar 

  12. Wang, M., Chen, S.C.: Enhanced FMAM based on empirical kernel map. IEEE Trans. Neural Netw. 16(3), 557–564 (2005)

    Article  Google Scholar 

  13. Feng, N.Q., Liu, C.H., Zhang, C.P.: Research on the framework of morphological associative memories. Chin. J. Comput. 33(1), 157–166 (2010)

    Article  Google Scholar 

  14. Feng, N.Q., Tian, Y., Wang, X.F.: Logarithmic and exponential morphological associative memories. J. Software 26(7), 1662–1674 (2015)

    MathSciNet  Google Scholar 

  15. Feng, N.Q., Tian, Y., Feng, G.H.: An effective method of hetero associative morphological memories. J. Chin. Comput. Syst. 36(10), 2374–2378 (2015)

    Google Scholar 

  16. Feng, N.Q., Wang, X.F., Mao, W.T.: Heteroassociative morphological memories based on four-dimensional storage. Neurocomputing 116, 76–86 (2013)

    Article  Google Scholar 

  17. Feng, N., Yao, Y.: No rounding reverse fuzzy morphological associative memories. Neural Netw. World 6, 571–587 (2016)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported in part by the science and technology research project of Zhengzhou city (Grant No. 153PKJGG153).

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University of Industrial Technology, Zhengzhou, 451150, China

    Bin Sun & Naiqin Feng

  2. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Naiqin Feng

Authors
  1. Bin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naiqin Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Sun .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Sun, B., Feng, N. (2017). Exploring the New Application of Morphological Neural Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63309-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics