MULTIPLE STRATEGIES FOR PARAMETER ESTIMATION VIA A HYBRID METHOD: A COMPARATIVE STUDY

Abstract In this paper, two different strategies are considered for application of a previously proposed hybrid method designed to parameter estimation. This method combines the feedforward neural networks ability to produce initial parameter estimates close to the true values with the fast convergence of the Levenberg-Marquardt method using such estimates. The first strategy is of general applicability, while the second one is intended for models having a structure defined by various blocks in series. The neuromuscular blockade model parameter estimation problem is taken as the case study for comparing the performances of the two strategies.