A Study on Backpropagation in Artificial Neural Networks

AUTHORS

Ch Sekhar,Department of CSE, VIIT(A), AP, India University, India
P Sai Meghana,Department of CSE, VIIT(A), AP, India University, India

ABSTRACT

Innovation assumes essential job nowadays in human life to limit the manual work. Execution and exactness with innovation will be high. The Backpropagation neural framework is multilayered, feedforward neural framework and is by a full edge the most extensively utilized. It is moreover seen as one of the least demanding and most wide systems used for managed planning of multilayered neural systems. Backpropagation works by approximating the non-direct association between the data and the yield by changing the weight regards inside. It can furthermore be summarized for the data that is rejected from the planning structures (perceptive limits).

 

KEYWORDS

Backpropagation, ANN, Neuron, Nervous system, MLP, Feedforward networks

REFERENCES

[1]     Budiharjo S. Triyuni W. Agus Perdana, and H. Tutut, “Predicting tuition fee payment problem using backpropagation neural network model,” (2018)
[2]     M. Huan, C. Ming, and Z. Jianwei, “Study on the prediction of real estate price index based on hhga-rbf neural network algorithm,” International Journal of u - and e-Service, Science and Technology, SERSC Australia, ISSN: 2005-4246 (Print); pp.2207-9718 (Online), vol.8, no.7, July, (2015) DOI: 10.142 57/ijunnes st.2015.8.7.11.(CrossRef)(Google Scholar)
[3]     A. Muhammad, A. Khubaib Amjad, and H. Mehdi, “Application of data mining using artificial neural network: survey,” International Journal of Database Theory and Application, vol.8, no.1, (2015) DOI: 10.14257/ijdta.2015.8.1.25.(CrossRef)(Google Scholar)
[4]     P. Jong, “The characteristic function of CoreNet (Multi-level single-layer artificial neural networks),” Asia-Pacific Journal of Neural Networks and Its Applications, vol.1, no.1, (2017) DOI: 10.21742/AJNNIA.201 7.1.1.02(CrossRef)(Google Scholar)
[5]     L. Wei, “Neural network model for distortion buckling behaviour of cold-formed steel compression members,” Helsinki University of Technology Laboratory of Steel Structures Publications 16, (2000)
[6]     The concept of Back-Propagation Learning by examples from the http://hebb.cis.uoguelph.ca/~skremer /Teachin g/27642/BP/node3.html

CITATION

  • APA:
    Sekhar,C.& Meghana,P.S.(2020). A Study on Backpropagation in Artificial Neural Networks. Asia-Pacific Journal of Neural Networks and Its Applications, 4(1), 21-28. 10.21742/AJNNIA.2020.4.1.03
  • Harvard:
    Sekhar,C., Meghana,P.S.(2020). "A Study on Backpropagation in Artificial Neural Networks". Asia-Pacific Journal of Neural Networks and Its Applications, 4(1), pp.21-28. doi:10.21742/AJNNIA.2020.4.1.03
  • IEEE:
    [1] C.Sekhar, P.S.Meghana, "A Study on Backpropagation in Artificial Neural Networks". Asia-Pacific Journal of Neural Networks and Its Applications, vol.4, no.1, pp.21-28, Aug. 2020
  • MLA:
    Sekhar Ch and Meghana P Sai. "A Study on Backpropagation in Artificial Neural Networks". Asia-Pacific Journal of Neural Networks and Its Applications, vol.4, no.1, Aug. 2020, pp.21-28, doi:10.21742/AJNNIA.2020.4.1.03

ISSUE INFO

  • Volume 4, No. 1, 2020
  • ISSN(p):2207-8738
  • ISSN(e):2207-8746
  • Published:Aug. 2020

DOWNLOAD