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Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks

Zulqurnain Sabir1, Manoj Gupta2, Muhammad Asif Zahoor Raja3, N. Seshagiri Rao4, Muhammad Mubashar Hussain5, Faisal Alanazi6, Orawit Thinnukool7, Pattaraporn Khuwuthyakorn7,*

1 Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
2 Department of Electronics and Communication Engineering, JECRC University, Jaipur (Rajasthan), 303905, India
3 Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
4 Department of Applied Mathematics, School of Applied Natural Sciences, Adama Science and Technology University, Adama, Ethiopia
5 Department of Mathematics, University of Punjab, Jhelum Campus, Pakistan
6 Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, College of Engineering, Al Kharj, 16278, Saudi Arabia
7 Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

* Corresponding Author: Pattaraporn Khuwuthyakorn. Email:

(This article belongs to this Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)

Computers, Materials & Continua 2022, 72(1), 1627-1644.


The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model (NSM) using the supervised neural networks (SNNs) along with the novel features of Levenberg-Marquardt backpropagation technique (LMBT), i.e., SNNs-LMBT. The SNNs-LMBT is implemented with three different types of sample data, authentication, testing and training. The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75% for training, 15% for validation and 10% for testing, respectively. For the numerical measures of the nonlinear dynamics of the NSM, the Runge-Kutta scheme is implemented to form the reference dataset. The attained numerical form of the nonlinear dynamics of the NSM through the SNNs-LMBT is implemented in the reduction of the mean square error (MSE). For the exactness, competence, reliability and efficiency of the proposed SNNs-LMBT, the numerical actions are capable using the proportional arrangements through the features of the MSE results, error histograms (EHs), regression and correlation.


Cite This Article

Z. Sabir, M. Gupta, M. Asif Zahoor Raja, N. Seshagiri Rao, M. Mubashar Hussain et al., "Nonlinear dynamics of nervous stomach model using supervised neural networks," Computers, Materials & Continua, vol. 72, no.1, pp. 1627–1644, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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