The effect of the threshold function for design feed forward neural network for solving Partial differential equations

Authors

  • Dr.Khalid. Mindeel. M Al-Abrahemee sity of AL-Qadisiyha

DOI:

https://doi.org/10.31642/JoKMC/2018/040103

Keywords:

partial differential equations, , Artificial neural network, Levenbrg- Marquardt training., feed forward neural network

Abstract

In this paper we disperse the outcome of threshold functions for designate feed forward neural network for solution partial differential equationsâ€. “We utility a multi-layer network having one hidden layer with 7 hidden units (neurons) and one linear output unit with different of threshold function of each unit are logsig , tansig, purelin, tribas and hardlim and use Levenberg – Marquardt (trainlm) training algorithmic ruleâ€. Finally the terminate of numerical experience are compare to with the true solution in illustrative examples to ratify the precision and effectiveness of the immediate plan

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References

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Published

2017-03-30

How to Cite

Al-Abrahemee, D. M. M. (2017). The effect of the threshold function for design feed forward neural network for solving Partial differential equations. Journal of Kufa for Mathematics and Computer, 4(1), 13–22. https://doi.org/10.31642/JoKMC/2018/040103

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