No abstract

No abstract

We present a method for estimating parameters on a nonlinear system that would otherwise be dificult to measure directly. The method is based on an eztended back-propagation technique where the evolution of the measured field variables over time is mapped to an artificial neural network. The connections within the network are defined by the mathematical model that represents the system. The model is then used to run forward simulations and inverse refinements iteratively until errors are within acceptable bounds.As an example, we investigate the performance of this method on a simulated 2 0 myocardial tissue. A modified FittHugh-Nagumo model is used where both the electrical potential (E) and the generalized current ( I ) are described over time. The task assigned to the method is to determine the cell-to-cell coupling or diffusion coeflcients of the simulated tissue.