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This paper presents a methodology for parameter estimation of a nonlinear neuromuscular blockade dynamic model to be used as a predictive model for automated control, in general anesthesia. The neuromuscular blockade dynamic model comprises two blocks connected in series, a pharmacokinetic model and the pharmacodynamic model. The pharmacokinetic model is a second order linear dynamic model and describes the redistribution of the drug in the body. The pharmacodynamic model is a nonlinear function, named as the Hill equation, and it describes the interaction between the concentration of the drug in the effect site and the measured patient's muscle paralysis state. The identification methodology uses four data points taken from the neuromuscular blockade response obtained with the administration of the first bolus. The four data points are chosen to avoid the identification difficulties caused by the presence of the nonlinear behavior of the Hill equation. This approach enables the identification of the pharmacokinetic dynamics, that is, the two poles of the second order linear dynamic model followed by the estimation of the normalized parameters of the Hill equation. Computer simulations show that the proposed identification methodology is able to provide good results even when the pharmacokinetic dynamics has an order higher that two. This suggests that the methodology may be employed in neuromuscular blockade automated control as a predictive model, to help the initial tuning of the controller parameters or in adaptive control to get a first model that can be improved with online identification using some recursive minimization techniques to adjust the adaptive controller or as an advising mechanism to help the anesthesiologist during the anesthesia.