期刊
CMC-COMPUTERS MATERIALS & CONTINUA
卷 74, 期 2, 页码 2575-2588出版社
TECH SCIENCE PRESS
DOI: 10.32604/cmc.2023.029046
关键词
Fractional order HBV differential infection system; artificial neu-ral networks; nonlinear; Levenberg-Marquardt backpropagation; Adams-Bashforth-Moulton
The investigations aim to solve the fractional order HBV differential infection system (FO-HBV-DIS) with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation neural networks (LMBNNs). The FO-HBV-DIS with the response of antibody immune is categorized into five dynamics: healthy hepatocytes, capsids, infected hepatocytes, free virus, and antibodies. Through numerical tests with three different fractional order variants, the nonlinear FO-HBV-DIS with the response of antibody immune is solved. The stochastic LMBNNs procedures in conjunction with the Adams-Bashforth-Moulton approach are utilized for numerical simulations and comparison of the results.
The current investigations are presented to solve the fractional order HBV differential infection system (FO-HBV-DIS) with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation (LMB) neural networks (NNs), i.e., LMBNNs. The FO-HBV-DIS with the response of antibody immune is categorized into five dynamics, healthy hepatocytes (H), capsids (D), infected hepatocytes (I), free virus (V) and antibodies (W). The investigations for three different FO variants have been tested numerically to solve the nonlinear FO-HBV-DIS. The data magnitudes are implemented 75% for training, 10% for certification and 15% for testing to solve the FO-HBV-DIS with the response of antibody immune. The numerical observations are achieved using the stochastic LMBNNs procedures for soling the FO-HBV-DIS with the response of antibody immune and comparison of the results is presented through the database Adams-Bashforth-Moulton approach. To authenticate the validity, competence, consistency, capability and exactness of the LMBNNs, the numerical presentations using the mean square error (MSE), error histograms (EHs), state transitions (STs), correlation and regression are accomplished.
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