Accurate models are necessary in critical applications. Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This dissertation presents a new approach to update system models, accounting for these changes. The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data. Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of model parameters that yield the best match between experimental and analytical responses. Since the minimization procedure requires a fair amount of computation time, it makes the existing techniques infeasible for use as part of an adaptive control scheme correcting the model parameters as the system changes. They also require either mode shape expansion or model reduction before they can be applied, introducing errors in the procedure. Furthermore, none of the existing techniques has been applied to nonlinear systems. The neural network estimates the parameters being updated quickly and accurately without the need to measure all degrees of freedom of the system. This avoids the use of mode shape expansion or model reduction techniques, and allows for its implementation as part of an adaptive control scheme. The proposed technique is also capable of updating weakly nonlinear systems. Numerical simulations and experimental results show that the proposed method has good accuracy and generalization properties, and it is therefore, a suitable alternative for the solution of the model updating problem of this class of systems. / Ph. D.