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<p>The goal of this conducted study is to provide the arithmetical performances through the stochastic computing procedure for the anthrax disease in animals (ADiA) model, which splits the populations between vaccinated, infected, susceptible, and recovered. A specific type of neural network, which is the novel radial basis is exploited by the radial basis and twenty-two neurons in the neural network’s hidden layer along with the optimization of Levenberg-Marquardt Backpropagation for solving the ADiA model. An Adam solver is generated to get the dataset and minimize the mean square error by dividing the data into testing as 14%, training as 75%, and corroboration as 11%. The exactness of the proposed solver is performed by using the overlapping of the outputs and an absolute error calculated as small. The test performance-based regression, state transition and error histogram also improve the dependability of the designed solver.</p>

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A novel radial basis neural network process for the numerical solutions of the anthrax disease model

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Zulqurnain Sabir,
  • Nazek El Halabi,
  • Mike Rizk,
  • Issa Kadri,
  • Muhammad Umar,
  • Soheil Salahshour

摘要

The goal of this conducted study is to provide the arithmetical performances through the stochastic computing procedure for the anthrax disease in animals (ADiA) model, which splits the populations between vaccinated, infected, susceptible, and recovered. A specific type of neural network, which is the novel radial basis is exploited by the radial basis and twenty-two neurons in the neural network’s hidden layer along with the optimization of Levenberg-Marquardt Backpropagation for solving the ADiA model. An Adam solver is generated to get the dataset and minimize the mean square error by dividing the data into testing as 14%, training as 75%, and corroboration as 11%. The exactness of the proposed solver is performed by using the overlapping of the outputs and an absolute error calculated as small. The test performance-based regression, state transition and error histogram also improve the dependability of the designed solver.