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- W4297833943 abstract "<abstract> <p>The purpose of this work is to present the stochastic computing study based on the artificial neural networks (ANNs) along with the scaled conjugate gradient (SCG), ANNs-SCG for solving the predator-prey delay differential system of Holling form-III. The mathematical form of the predator-prey delay differential system of Holling form-III is categorized into prey class, predator category and the recent past effects. Three variations of the predator-prey delay differential system of Holling form-III have been numerical stimulated by using the stochastic ANNs-SCG procedure. The selection of the data to solve the predator-prey delay differential system of Holling form-III is provided as 13%, 12% and 75% for testing, training, and substantiation together with 15 neurons. The correctness and exactness of the stochastic ANNs-SCG method is provided by using the comparison of the obtained and data-based reference solutions. The constancy, authentication, soundness, competence, and precision of the stochastic ANNs-SCG technique is performed through the analysis of the correlation measures, state transitions (STs), regression analysis, correlation, error histograms (EHs) and MSE.</p> </abstract>" @default.
- W4297833943 created "2022-10-01" @default.
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- W4297833943 date "2022-01-01" @default.
- W4297833943 modified "2023-10-15" @default.
- W4297833943 title "Supervised neural learning for the predator-prey delay differential system of Holling form-III" @default.
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- W4297833943 doi "https://doi.org/10.3934/math.20221101" @default.
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