Skip to main content
SpringerLink
Log in

Evolutionary Operations of Interneuron Synaptic Structure for Feed-Forward Multilayer Networks

  • NEW TECHNOLOGIES IN MECHANICAL ENGINEERING
  • Published:
Journal of Machinery Manufacture and Reliability Aims and scope Submit manuscript

Abstract

This paper mainly studies evolutionary operations of interneuron synaptic structure search based on the developed structure synthesis algorithm for feed-forward multilayer network trained with a teacher. The decomposition of neural network learning tasks into a series of subtasks is proposed. During evolutionary search the neural network is not converted to a genotype and all the transformations of synaptic structures are performed directly. This differs from a classical approach to genetic algorithms involving coding phenotype of individuals (structure) to genotype (code string).

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Darwin, C., The Origin of Species, London: Murray, 1859.

    Google Scholar 

  2. Holland, J.H., Genetic algorithms, Sci. Am., 1992, vol. 267, no. 1, pp. 66–73.

    Article  Google Scholar 

  3. Khomich, A.V. and Stepanyan, I.V., Karpishuk blocking principle in evolutionary optimization neural network structures, Neurocomput.: Dev. Appl., 2006, no. 3, p. 17.

  4. Yurong Liu, et al., Exponential stability of Markovian jumping Cohen-Grossberg neural networks with mixed mode-dependent time-delays, Neurocomputing, 2016, vol. 177, p. 409.

    Article  Google Scholar 

  5. Bing Xu, Naiya Wang, Tianqi Chen, and Mu Li, Empirical Evaluation of Rectified Activations in Convolutional Network, 2015. arXiv:1505.00853 [cs.LG]

  6. Schmidhuber, J., Deep learning in neural networks: An overview, Neural Networks, 2015, vol. 61, pp. 85–117. https://doi.org/10.1016/j.neunet.2014.09.003

    Article  Google Scholar 

  7. Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, MIT Press, 2016.

    MATH  Google Scholar 

  8. Cauchy, A.L., Sur les formules qui résultent de l’emploie du signe et sur > ou <, et sur les moyennes entre plusieurs quantités, in Cours d’Analyse de l’École Royale Polytechnique; I.re Partie. Analyse Algébrique, 1821, pp. 373–377.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN), 101000, Moscow, Russia

    I. V. Stepanyan

Authors
  1. I. V. Stepanyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. V. Stepanyan.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stepanyan, I.V. Evolutionary Operations of Interneuron Synaptic Structure for Feed-Forward Multilayer Networks. J. Mach. Manuf. Reliab. 49, 874–877 (2020). https://doi.org/10.3103/S105261882010009X

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S105261882010009X

Keywords:

Search

Navigation