Open Access

ARTICLE

Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks

by Zulqurnain Sabir¹, Manoj Gupta², Muhammad Asif Zahoor Raja³, N. Seshagiri Rao⁴, Muhammad Mubashar Hussain⁵, Faisal Alanazi⁶, Orawit Thinnukool⁷, Pattaraporn Khuwuthyakorn^7,*

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 the Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)

Computers, Materials & Continua 2022, 72(1), 1627-1644. https://doi.org/10.32604/cmc.2022.021462

Received 03 July 2021; Accepted 29 October 2021; Issue published 24 February 2022

Abstract

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.

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Keywords

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