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.
Highlights
The nonlinear dynamics of the nervous stomach model (NSM) have three segments, Tension (T), Food (F) and Medicine (M), i.e., TFM model
The absolute error (AE) for M(y) is noticed in Fig. 14c that lie around 10−05 to 10−06 for case I and II, while the AE for case III lie around 10−07 to 10−08. These closely matched values of AE indicate the correctness of the proposed supervised neural networks (SNNs)-LevenbergMarquardt backpropagation technique (LMBT) to solve the nonlinear dynamics of the NSM based TFM system
The current investigations are related to solve the nonlinear dynamics of the nervous stomach model based on the three factors; Tension, Food and Medicine are using the proposed supervised neural networks along with the Levenberg-Marquardt backpropagation technique, i.e., SNNs-LMBT
Summary
The nonlinear dynamics of the nervous stomach model (NSM) have three segments, Tension (T), Food (F) and Medicine (M), i.e., TFM model. Few recent stochastic submissions are SITR dynamics [13,14], singular third kind of nonlinear system [15,16], Thomas–Fermi form of the model [17], heat conduction model [18], periodic differential singular system [19,20], functional models [21,22,23], dengue fever biological model [24], a multi-singular form of equations [25,26], prediction, delayed and pantograph models [27,28,29] and differential systems based on the fractional order [30,31,32] These well-known stochastic based applications inspire the authors to present a robust, consistent, accurate and reliable platform to solve the nonlinear dynamics of the NSM based TFM system using the SNNs-LMBT [33,34,35]. The concluding outcomes with latent related soundings together with the future research reports are labeled in the Section 4
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