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Numerical analysis and implementation of artificial neural network algorithm for nonlinear function

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Abstract

Artificial Neural Network (ANN) architecture contains three main (input, hidden and output) layers. The connections are established between layers through weights and bias. The various simulators are available to implement ANN. But, a knowledge on the numerical investigation and application of ANN for arbitrary values related by a nonlinear function is essential for the better understanding of a nature-inspired algorithm. Hence, the present manuscript provides the mathematical implementation of an algorithm for known values of independent and dependent variables, with and without bias. The predictability of ANN was assessed by a statistical parameter, mean squared error (MSE). The MSE by ANN algorithm was found to be higher (0.008) without bias than with bias for arbitrary values of independent and dependent variables. Hence, it could be concluded that the bias reduces error and enhances the predictability .

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Acknowledgements

We would acknowledge our heartfelt thanks to the Management of Salalah College of Technology, Oman, for the wonderful opportunity, continuing support and encouragement for writing the manuscript.

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Authors and Affiliations

  1. Information Technology Department, Salalah College of Technology, Salalah, Oman

    Pathamuthu Suresh Kumar

  2. Engineering Department, Salalah College of Technology, Salalah, Oman

    Selvaraju Sivamani

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  1. Pathamuthu Suresh Kumar
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  2. Selvaraju Sivamani
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Correspondence to Selvaraju Sivamani.

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Kumar, P.S., Sivamani, S. Numerical analysis and implementation of artificial neural network algorithm for nonlinear function. Int. j. inf. tecnol. 13, 2059–2068 (2021). https://doi.org/10.1007/s41870-021-00743-6

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  • DOI: https://doi.org/10.1007/s41870-021-00743-6

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