Título: A hybrid algorithm for parameter estimation of the ghrelin dynamics based on in vivo data
Autores: Pires, Jorge
Food intake, bodyweight and appetite are controlled by a “web of hormones.”On this work, we shall present a problem in parameter estimation using evolutionary algorithms alongside local search, what we have called hybrid algorithms (global search + local search); furthermore, we apply artificial neural networks (feedforward neural network) for supporting the numerical simulations (what we have called “fake data”). We present a mathematical model for ghrelin partially published elsewhere by the same authors; furthermore, we have confronted the model mathematically with in vivo data via parameter estimation and got promising results for the novel mathematical formulation. Notwithstanding the parameter estimation was unable to model precisely the experimental data, most likely due to physiological details still unclear in the medical literature, it generated an optimized curve relatively close to the experimental data, leaving promising results for future investigations.
Ghrelin;Parameter Estimation;Evolutionary Computing;Artificial Neural Networks;Food Intake;Appetite Control.
Código DOI: 10.21528/CBIC2017-9
Artigo em pdf: cbic-paper-9.pdf
Arquivo BibTeX: cbic-paper-9.bib