A hybrid method for parameter estimation and its application to biomedical systems

A general version of a hybrid method for parameter estimation is presented with a theoretical support and an illustrative example of application. This method consists of a curve fitting algorithm that takes the initial estimate of the parameterization from an artificial neural network. The idea is to improve the convergence of the algorithm to the sought parameterization using a close initial estimate. The motivation arises from biomedical problems where one is interested in obtaining a meaningful estimate so that it can be used for both description and prediction purposes. Two strategies are proposed for the application of the hybrid method: one is of general applicability, the other is intended for systems defined by the series connection of various blocks. The feasibility of the method is illustrated with a case study related to the neuromuscular blockade of patients undergoing general anaesthesia.